# MpoxSLDNet: A Novel CNN Model for Detecting Monkeypox Lesions and
Performance Comparison with Pre-trained Models

Fatema Jannat Dihan$1$, Saydul Akbar Murad$2$, Abu Jafar Md Muzahid$3$, K. M.
Aslam Uddin$1$, Mohammed J.F. Alenazi$4$, Anupam Kumar Bairagi$5$, and Sujit
Biswas$6$ $1$ Noakhali Science and Technology University, Sonapur, Noakhali,
3814, Bangladesh, $2$ School of Computing Sciences & Computer Engineering,
University of Southern Mississippi, Hattiesburg, MS, USA, $3$ Universiti
Malaysia Pahang, Pekan, Pahang, 26600, Malaysia, $4$ King Saud University,
Riyadh, 11451, Saudi Arabia, $5$ Khulna University, Khulna, 9208, Bangladesh
and $6$ Cybersecurity and FinTech Computer Science Department, City,
University of London, EC1V 0HB, UK.

###### Abstract

Monkeypox virus (MPXV) is a zoonotic virus that poses a significant threat to
public health, particularly in remote parts of Central and West Africa. Early
detection of monkeypox lesions is crucial for effective treatment. However,
due to its similarity with other skin diseases, monkeypox lesion detection is
a challenging task. To detect monkeypox, many researchers used various deep-
learning models such as MobileNetv2, VGG16, ResNet50, InceptionV3,
DenseNet121, EfficientNetB3, MobileNetV2, and Xception. However, these models
often require high storage space due to their large size. This study aims to
improve the existing challenges by introducing a CNN model named MpoxSLDNet
(Monkeypox Skin Lesion Detector Network) to facilitate early detection and
categorization of Monkeypox lesions and Non-Monkeypox lesions in digital
images. Our model represents a significant advancement in the field of
monkeypox lesion detection by offering superior performance metrics, including
precision, recall, F1-score, accuracy, and AUC, compared to traditional pre-
trained models such as VGG16, ResNet50, and DenseNet121. The key novelty of
our approach lies in MpoxSLDNet’s ability to achieve high detection accuracy
while requiring significantly less storage space than existing models. By
addressing the challenge of high storage requirements, MpoxSLDNet presents a
practical solution for early detection and categorization of monkeypox lesions
in resource-constrained healthcare settings. In this study, we have used
"Monkeypox Skin Lesion Dataset" comprising 1428 skin images of monkeypox
lesions and 1764 skin images of Non-Monkeypox lesions. Dataset’s limitations
could potentially impact the model’s ability to generalize to unseen cases.
However, the MpoxSLDNet model achieved a validation accuracy of 94.56%,
compared to 86.25%, 84.38%, and 67.19% for VGG16, DenseNet121, and ResNet50,
respectively. In addition, an average precision, recall, F1 score of 0.94
respectively, and AUC of 0.94 was recorded for our proposed model.

###### Index Terms:

Monkeypox, Deep Learning, DGG16, ResNet50, MpoxSLDNet, CNN

##  I Introduction

Figure 1 provides an introductory overview of the efforts to enhance monkeypox
lesion detection. It begins by elucidating the characteristics of the
Monkeypox virus including its origins, classification, symptoms, and modes of
transmission. Monkeypox virus (MPXV) is a zoonotic virus that is predominantly
found in distant Central and West African regions . It was first identified in
a monkey in 1959, when outbreaks of a disease resembling pox were observed in
monkeys retained for research purposes, in a laboratory in Copenhagen, at a
Danish research institute, and thus the name Monkeypox virus [2, 3]. It is a
type of DNA virus classified within the orthopoxvirus genus and is a member of
the family of Poxviridae, is closely linked to the Variola virus, which is the
infectious illness that causes monkeypox [4]. In 1970, the first recorded
instance of monkeypox cases in humans was documented in the Democratic
Republic of the Congo, within an intensified campaign to wipe out smallpox.
The monkeypox virus falls into two groups: Congo Basin (Central African) and
West African. According to a previous report, the Congo Basin clade tends to
produce severe disease more often, with case fatality ratios (CFR) of up to
10%. As of right now, the Democratic Republic of Congo reports a 3% Case
Fatality Ratio among suspected cases. In the past, the West African clade has
been attributed to a lower overall CFR of roughly 1% in an African population
who are normally younger. An average incubation period is 12 days for MPXV,
yet it might vary from 5 to 21 days. [5]. Monkeypox virus signs involve fever,
headache, tenderness in the muscles, backaches, swelling of the lymph nodes,
chills, as well as exhaustion [6, 7, 31]. The monkeypox virus circulates from
a human to another human being thru droplets from the nose or throat,
interaction with affected bodily fluids or contaminated surfaces, infectious
rash, and sexual contact. This virus can also spread through the handling of
infected animals [8].

There is unfortunately no medication or vaccination specifically against the
monkeypox virus. Although supportive care can be given to combat symptoms and
undesirable side effects. Antiviral drugs can be used that are used for
treating other orthopoxvirus infections [9]. A skin lesion test utilizing the
technique of electron microscopy or polymerase chain reaction, also known as a
PCR test is time-consuming and takes a long time to detect pox [10]. The
insufficient availability of PCR test kits, as well as the strict requirements
for laboratory settings in research facilities, will delay the exact
identification of suspects [11].

The detection of monkeypox lesions can be challenging due to the similarity
that exists between monkeypox lesions and another pox [12, 13], which can lead
to misdiagnosis. In addition, personnel in healthcare might have been
unfamiliar with a clinical presentation of the lesions of monkeypox [14],
which can further complicate accurate diagnosis. Moreover, physical
examinations to diagnose monkeypox can increase the risk of transmission to
healthcare professionals [15] and other patients. According to [16], isolation
is recommended for a patient with a suspected monkeypox infection.

To address all these challenges, there is a need for the development of a
reliable and accurate diagnostic tool that can quickly and non-invasively
detect and classify monkeypox lesions from other poxvirus infections which
also can significantly reduce the risk of transmission to healthcare
professionals and other patients. Neural networks can help in this situation.
With advances in the development involving AI models, many models based on
deep learning have been utilized in the identification of various diseases
[17, 18, 19, 30]. So, in our current research, we have developed a model named
“MpoxSLDNet” based on convolutional neural networks (CNNs) to automatically
detect monkeypox lesions from digitized pictures of human skin lesions.

Figure 1: Navigating Monkeypox Lesion Detection: An Introduction Overview.

From the existing research work regarding monkeypox lesion detection, we can
observe, in [1], authors used publicly available datasets. They presented an
Android mobile application in which low modified deep learning model
MobileNetv2 is used to classify the monkeypox lesion and non-monkeypox lesions
with 91.11% accuracy. In [11], authors developed a monkeypox skin lesion
dataset, and they employed deep learning networks including VGG16, ResNet50,
InceptionV3, and Ensemble. In their study, among the evaluated models,
ResNet50 demonstrated the highest overall accuracy, achieving an accuracy of
82.96%. Also VGG16, InceptionV3,Ensemble obtained accuracy of 81.48%,
74.07%,79.26% respectively. Here, the authors presented a web app that used
the best-performing model. Apart from this, Authors [20] used publicly
available datasets and employed five deep learning models, including
DenseNet121, VGG19, MobileNetV2, EfficientNetB3, and Xception combined with
the CBAM and Dense layers for categorization. Here, VGG19, DenseNet121,
EfficientNetB3, MobileNetV2, and Xception achieved accuracy 71.86%, 78.27%,
81.43%, 74.07%, 83.89% respectively. Despite the significant efforts in the
existing research work regarding monkeypox lesion detection, our model
achieves superior results on the basis of accuracy, precision, recall,
F1-score, and AUC compared to the state-of-the-art models. While the existing
thesis work on monkeypox lesion detection has employed several deep-learning
models, these models require significant storage space due to their large
number of parameters and also require more computational time. In contrast,
our proposed CNN model is designed to be both accurate and efficient, with
fewer parameters that require a smaller amount of storage as well as
computational time. This makes our approach more practical for use in
resource-constrained settings, such as remote areas where monkeypox outbreaks
are more likely to occur.

Our proposed MpoxSLDNet model is a Sequential neural network containing max
pooling, convolutional layers, batch normalization, dropout, as well as fully
connected layers. It takes input images of size 224x224x3 and learns to
classify them as either having monkeypox lesions or not. The feature
extraction process in our model involves using a series of convolutional
layers of adding depth in addition to reducing spatial dimensions, commencing
with max pooling layers to reduce the dimensionality of the feature maps. The
extracted features are then flattened and passed through various fully
connected layers with batch normalization and dropout regularization to stay
away from overfitting. The final layer of output uses a sigmoid activation
function to acquire a binary classification result. The contribution of this
study to the field of monkeypox lesion detection is listed below:

  * •

Developed ’MpoxSLDNet’, a convolutional neural network model, for automated
identification of monkeypox lesions in digitized human skin lesion imagery,
utilizing advanced image processing and deep learning techniques.

  * •

Validated ’MpoxSLDNet’s effectiveness in precisely distinguishing monkeypox
lesions from other poxvirus infections, outperforming existing pre-trained
models in accuracy through rigorous dataset comparisons and performance
metrics analysis.

  * •

Introduced a contactless, non-invasive diagnostic approach, significantly
reducing transmission risks to healthcare professionals and patients,
utilizing advanced sensor technologies and AI-driven analysis.

The rest of this study is organized as follows: section 2 describes and
discusses the literature review. Section 3 provides details about the
methodology in which several methodological steps were undertaken, including
data collection, preprocessing, the architecture of the MpoxSLDNet model, and
the evaluation method. Section 4 evaluates the results. This section provides
a comprehensive analysis of the outcomes obtained from our suggested model and
compares them with pre-trained models, including DenseNet121, ResNet50, and
VGG16, highlighting its effectiveness in detecting monkeypox lesions. Finally,
this paper is concluded in Section 5.

##  II Literature Review

Monkeypox is a rare and serious viral disease that presents a significant
public health risk. The timely detection of monkeypox lesions is essential for
effective treatment as well as outbreak prevention. Deep learning-based models
have gained prominence in recent years due to their potential use in
automating the detection of monkeypox lesions. A recent study conducted by [4]
collected monkeypox images from various sources and developed a dataset of
monkeypox images that are publicly available. This study established a
modified VGG16 model to classify monkeypox patients and non-monkeypox
patients. Post-image analysis explanations using a technique called Local
Interpretable Model-Agnostic Explanations (LIME) were also used in order to
verify outcomes. The modified VGG16 model was evaluated for two distinct
studies, including study one (without augmentation) and study two (with
augmentation). The achieved accuracies for studies one and two were 97 ± 1.8%
and 88 ± 0.8%, respectively. However, this study only considered the modified
VGG16 model. Additionally, Another study conducted by [21], proposed a model
named MonkeypoxHybridNet based on CNN, which is comprised of VGG19, ResNet50,
and InceptionV3. The performance of the model was assessed by evaluating its
precision, F1 score, and accuracy against three pre-trained models, including
VGG19, ResNet50, and InceptionV3. The achieved accuracy for VGG19, ResNet50,
InceptionV3, and MonkeypoxHybridNet were 0.595, 0.705, 0.80, and 0.842,
respectively. The proposed model showed significant results, but it has some
limitations. The authors only presented accuracy, precision, F1 score, and
confusion matrix but did not show the Recall and AUC score for the proposed
MonkeypoxHybridNet model. In a subsequent study by [14], a variety of pre-
trained models were used to distinguish between chickenpox and monkeypox
lesions, including Inception V3, ResNet50, VGG16, VGG19, and AlexNet. They
also implemented a CNN model. The chickenpox dataset used in the study was
publicly available, while the monkeypox dataset was obtained through web
scraping.VGG16 and VGG19 accuracy was also 80%, while ResNet50, AlexNet, and
Inception V3 each provided accuracy results of 82.00%, 98.00%, and 89.00%,
respectively. The recorded accuracy of the proposed CNN model was 99%.
However, the AUC score, which is an important measure of classifier
performance, was not calculated in this study. Apart from this, another study
by [2] compared the performance of 13 pre-trained models to detect the
monkeypox lesion. Universal custom layers had been used for all models, and
the performance was analyzed based on four metrics: accuracy, precision,
recall, and F1-score. The study identified DL models that exhibited the
highest performance and ensembled them via majority voting on their
probabilistic outputs to enhance the overall performance. The experiments
utilized a dataset that is publicly accessible and the proposed ensemble
method provided accuracy, precision, recall, and F1-score of 87.13%, 85.44%,
85.47%, and 85.40%, respectively. However, this study did not propose any
lightweight CNN models, and the AUC score was not considered. In a subsequent
study conducted by [11], A dataset called the Monkeypox Skin Lesion Dataset
(MSLD) was created, which includes images of skin lesions associated with
monkeypox, chickenpox, and measles. Three widely used pre-trained models,
including InceptionV3, ResNet50, and VGG16, were utilized to identify
monkeypox and non-monkeypox patients. Additionally, an ensemble of three
models (InceptionV3, ResNet50, and VGG16) was employed in this study. The
achieved accuracies were 81.48% (±6.87%), 82.96% (±4.57%), 74.07% (±3.78%),
and 79.26% (±1.05%) for VGG16, ResNet50, InceptionV3, and the ensemble method,
respectively. Furthermore, a web application was employed as an online
monkeypox screening tool. It is known that pre-trained models require more
storage space and computational time. In this work, only pre-trained models
were utilized, and no lightweight CNN models were established. As for
performance evaluation metrics, the AUC score was not considered. Furthermore,
a study conducted by [22], used five pre-trained models including VGG16,
VGG19, MobileNetV2, ResNet50, and EfficientNetB3. The performance of the model
is assessed by measuring its accuracy, recall, precision, and F1 score. Their
experimental result demonstrates that among the five pre-trained models
MobileNetV2 gives the best classification performance. Achieved accuracy,
precision, recall, and F1-score of MobileNetV2 are 98.16%, 0.99, 0.96, and
0.98. Their objective was to select the best-performing model to detect and
classify monkeypox. In this study, the authors don’t consider the AUC score
and don’t show the multiple training and testing accuracy. They also don’t
discuss losses during training. Another study employed by [23], a newly
curated image dataset, referred to as the "Monkeypox skin images dataset," was
developed, encompassing four classes: monkeypox, chickenpox, measles, and
normal images. In this study, five machine learning classifiers, including
Logistic Regression, Random Forest, Support Vector Machine, K-Nearest
Neighbor, as well as Extreme Gradient Boosting were used for classification.
For feature extraction, VGG16, ResNet50, MobileNetV1, Inception V3, Xception.
Also, they presented a novel CNN model called MonkeyNet, which is a modified
version of DenseNet-201, and evaluated its performance. The performance of the
model was assessed using various metrics including accuracy, precision,
recall, F1-score, and AUC. Resulted in accuracy for the original dataset and
augmented dataset of the proposed model are 91.91% and 98.91%, consecutively.
Another study conducted by [20] utilized five deep learning models including
VGG19, Xception, DenseNet121, MobileNetV2, and EfficientNetB3.To utilize a
more relevant feature map, they integrate the deep learning model with the
Convolutional Block Attention Module. This module encompasses mechanisms such
as channel attention and spatial attention-based mechanism. Then dense layer,
flattened layer, and classification layer are added to each model to classify
monkeypox and others. The model’s performance is assessed using accuracy,
precision, recall, and F1-score as evaluation metrics. Among the various
combinations, Xception-CBAM-Dense yielded the highest performance with a
validation accuracy of 83.89%, precision of 90.70%, recall of 89.10%, and
F1-score of 90.11%. Authors [33] used deep learning techniques, specifically
deep neural networks, for the detection of monkeypox virus using skin lesion
images. They tested the dataset on five pre-trained deep neural networks:
GoogLeNet, Places365-GoogLeNet, SqueezeNet, AlexNet, and ResNet-18. Transfer
learning models such as Xception, DenseNet-169, and MobileNetV2 were also
employed in other studies related to monkeypox diagnosis. Data augmentation
techniques were applied to increase the size of the dataset and improve the
performance of the deep neural networks. The deep learning models achieved
high accuracy in detecting monkeypox virus from skin lesion images, with
ResNet-18 obtaining the highest accuracy of 99.49%. Other performance metrics
such as precision, recall, f1-score, and AUC were also considered, and the
modified models obtained validation accuracies above 95%. Authors [34]
employed deep convolutional neural networks of the residual network family,
including ResNet-18, ResNet-50, and ResNet-101, to solve the vanishing
gradient problem and achieve high performance in image classification. They
also used SqueezeNet as a baseline model. The researchers achieved an average
accuracy of 91.19% and an F1-score of 92.55% for the Monkeypox class using
deep learning models. The researchers also used explainable artificial
intelligence (XAI) techniques, such as LIME (Local Interpretable Model-
Agnostic Explanations), to visualize the predictions made by the convolutional
neural network. Authors [34] integrate deep neural networks with federated
learning (FL) to address the challenges of medical data categorization.
Also,they used a cycle-consistent generative adversarial network (GAN) to
augment data samples for training. And utilize deep learning models such as
MobileNetV2, Vision Transformer (ViT), and ResNet50 for the classification
task. Authors [36] developed a hybrid artificial intelligence system using
deep learning models (CSPDarkNet, InceptionV4, MnasNet, MobileNetV3, RepVGG,
SE-ResNet, and Xception) and a long short-term memory (LSTM) model for
monkeypox detection. The classification results were analyzed using metrics
such as test accuracy and Cohen’s kappa score.The proposed hybrid artificial
intelligence system achieved a test accuracy of 87% and a Cohen’s kappa score
of 0.8222 for monkeypox detection. [37] presented a model, PoxNet22, by fine-
tuning the InceptionV3 to classify monkeypox from other poxes. They used
different preprocessing techniques to improve the image quality and used
augmentation to reduce the possibility of overfitting. They got very high
precession, recall and accuracy. [38] used transfer learning to assess the
performance of different DL mehtods namely, VGG16, InceptionResNetV2,
ResNet50, ResNet101, MobileNetV2, VGG19, and Vision Transformer (ViT) for
diagnosing monkeypox. Among their models, modified VGG16 and MobileNetV2
outperformed others. For proving model’s transparency, they used LIME with
their model. [39] presented a model for detecting monkeypox by using
attention-based MobileNetV2. They used diversified performance parameters to
asses the superiority of the proposed model. They also used Grad-CAM and LIME
for ensuring transparency and interpretability of their model.

Table I presents a comprehensive comparison of various parameters employed in
previous studies and our study focused on monkeypox lesion detection. Each row
corresponds to a specific study, while the columns represent different
evaluation metrics that include accuracy, precision, recall, F1-score, area
under the curve (AUC), etc. This analysis offers valuable insights into the
existing literature and highlights the unique contribution of our study by
incorporating all key evaluation metrics for a comprehensive evaluation of
monkeypox lesion detection. Our study aims to contribute to the existing
literature on monkeypox lesion detection by providing a comprehensive
comparison of key evaluation metrics, including multiple training
accuracy,multiple testing accuracy, Training loss, Testing Loss, precision,
recall, F1-score, and area under the curve (AUC). As demonstrated in Table I,
our analysis revealed that several performance metrics were not consistently
reported across existing studies. This observation underscores the need for a
more standardized and comprehensive approach to evaluating monkeypox lesion
detection models. By systematically comparing the performance of various pre-
trained models and proposing a novel lightweight CNN model, MpoxSLDNet, our
study addresses the limitations identified in previous research. Specifically,
we have incorporated all key evaluation metrics, including multiple training
accuracy,multiple testing accuracy, Training loss, Testing Loss, precision,
recall, F1-score, and area under the curve (AUC), which were often overlooked
in previous studies. This holistic evaluation approach provides a more robust
assessment of model performance and facilitates a deeper understanding of the
strengths and weaknesses of different monkeypox lesion detection
methodologies.

TABLE I: Comparative analysis of parameters used in existing studies with our
study.

Ref. |  Multiple training accuracy |  Multiple Testing Accuracy |  Training Loss |  Testing Loss |  Precision |  Recall |  F1-Score |  Accuracy |  AUC-ROC Curve
---|---|---|---|---|---|---|---|---|---
[4] |  ✗ |  ✗ |  ✗ |  ✗ |  ✓ |  ✓ |  ✓ |  ✓ |  ✓
[21] |  ✗ |  ✗ |  ✗ |  ✗ |  ✓ |  ✗ |  ✓ |  ✓ |  ✗
[14] |  ✗ |  ✗ |  ✗ |  ✓ |  ✓ |  ✓ |  ✓ |  ✓ |  ✗
[2] |  ✗ |  ✗ |  ✓ |  ✓ |  ✓ |  ✓ |  ✓ |  ✓ |  ✗
[11] |  ✗ |  ✗ |  ✗ |  ✗ |  ✓ |  ✓ |  ✓ |  ✓ |  ✗
[22] |  ✗ |  ✗ |  ✗ |  ✗ |  ✓ |  ✓ |  ✓ |  ✓ |  ✗
[20] |  ✗ |  ✗ |  ✗ |  ✗ |  ✓ |  ✓ |  ✓ |  ✓ |  ✗
[34] |  ✗ |  ✗ |  ✓ |  ✓ |  ✓ |  ✓ |  ✓ |  ✓ |  ✗
[33] |  ✗ |  ✗ |  ✗ |  ✗ |  ✓ |  ✓ |  ✓ |  ✓ |  ✗
Proposed Method |  ✓ |  ✓ |  ✓ |  ✓ |  ✓ |  ✓ |  ✓ |  ✓ |  ✓

##  III Methodology

The methodology section of this study outlines the process of data collection,
data mounting, data pre-processing, the proposed MpoxSLDNet model’s structure,
and the evaluation metrics used to compare the performance of MpoxSLDNet with
three pre-trained models, including VGG16, ResNet50, and DenseNet121. Firstly,
to achieve the objectives that are set out, the dataset of monkeypox skin
lesion images was collected and pre-processed to ensure consistency and
quality. Then the suggested CNN model MpoxSLDNet was built and three pre-
trained models including VGG16, ResNet50, and DenseNet121 were utilized for
transfer learning. Finally, The MpoxSLDNet model’s performance is assessed by
comparing it to the pre-trained models using standard evaluation metrics
including accuracy, precision, recall, and F1-Score, AUC-ROC curve. The
overall workflow of this study is shown in Figure 2.

Figure 2: Overall Workflow diagram.

###  III-A Data Collection

In this study, data are collected from a publicly accessible source known as
the "Monkeypox Skin Lesion Dataset" [24]. The dataset includes two classes:
Monkeypox and Non-Monkeypox, which are used for performing binary
classification. The Monkeypox class contains 1428 skin images of monkeypox
lesions. On the other hand, Non-Monkeypox class contains 1764 skin images of
Non-Monkeypox lesions. The detailed data description is shown in Table II.
Figure 3 illustrates a visual representation of the dataset’s image
distribution, highlighting the proportion of monkeypox lesion images compared
to non-monkeypox lesion images. This information is crucial for understanding
the composition of the dataset and the relative representation of different
lesion types, thus providing valuable insights into the data used for training
and evaluation in our study. Figure 4 shows the Sample set of images of
Monkeypox lesion and Non-Monkeypox lesion from the dataset.

111https://www.kaggle.com/datasets/nafin59/monkeypox-skin-lesion-dataset TABLE
II: Dataset Description.

Class |  Description |  Source of data
---|---|---
Monkeypox |  In this dataset, the monkeypox class contains 1428 skin images of monkeypox lesions. |  Kaggle Datasets [24]
Non-Monkeypox |  Non-Monkeypox class contains 1764 Skin images of chickenpox lesions, measles lesions. |  Kaggle Datasets [24]

Figure 3: Distribution of Monkeypox lesion and Non-Monkeypox lesion images in
the dataset. Figure 4: Sample set of images from dataset.

###  III-B Data Pre-processing

This step is crucial for the deep learning model as it ensures that the input
data is appropriately formatted for training the model, leading to enhanced
model accuracy. In this step, collected data are passed through six pre-
processing operations before it is fed into the model. These pre-processing
operations include resizing images to uniform sizes, rescaling the pixel
value, setting data_format, label encoding, in-place augmentation, and data
[32]. All pre-processing operations are described here.

####  III-B1 Resizing Image

Image resizing is the process of resampling or downsampling the image. Images
in the dataset should have the same size as the CNN model. So, these stages
are used to assure that all the images have the same size. This stage is also
used to reduce the computational burden of training the model. This is
accomplished with the aid of Python/TensorFlow.keras.preprocessing module. In
this study, image dimensions are set to 224×224 dimension. The reason for
selecting image size 224×224 is that this size allows better performance, more
accurate training, and faster convergence of model [26].

####  III-B2 Rescale:

Rescaling is the process of adjusting or normalizing the image data to a
specific range of 0 to 1. ImageDatagenerator class is used to transform the
pixel values of images from the original range of 0 to 255 into the desired
range of 0 to 1, which is more suitable for neural network models. Different
image has different pixel value. If rescale operation is not applied, the
model only encounters certain images which leads to overfitting. This
operation ensures that each image is given equal importance and weight during
the model’s training process. This operation can be accomplished by specifying
the rescale argument which involves multiplying each pixel by a specific ratio
to achieve the desired scaling. In this scenario, the ratio used for rescaling
the pixel values is 1/255, which is approximately equal to 0.0039 [22].

####  III-B3 Data_format:

In this study, data_format is set to "channels_last" which means that the last
dimensions of the input tensor are channels. In this "channels_last" format
image is depicted as a 3D tensor that corresponds to (height, width, and
channels). In deep learning frameworks like TensorFlow and Keras, this format
is the default value in Keras. During training and evaluating the model, this
format ensures compatibility, efficient computation, and data manipulation
[27].

####  III-B4 Label Encoding:

Label encoding is a process of transforming categorical labels into numerical
representations, enabling deep-learning predictive models to comprehend them
effectively. In this study, the dict and zip method is used to create a
mapping between the two classes ‘Monkeypox’ and ‘Non_Monkeypox’ and their
corresponding numerical values 0 and 1. By using the dict method, we have
created a dictionary in which the keys are the numerical values and the values
are corresponding categorical labels. The zip method then makes a list of rows
by combining the numerical values with the categorical labels. With the
resulting dictionary, the categorical labels can be transformed into numerical
values, allowing the model to utilize these numerical representations for
making predictions. [25].

####  III-B5 Data Augmentation (in-place):

In this pre-processing operation, random image transformations performed by
in-place data augmentation (on-the-fly) have been applied, which means that
the dataset size does not increase. In this type of data augmentation, at the
time of training, our network observes unique variations of our data during
each epoch and captures different patterns and features with each iteration.
To transform the image, ImageDatagenerator, a Keras image prepossessing
library, is used. Image transformation operations available in the
ImageDatagenerator function include brightness_range, zoom range, horizontal
flip, etc. Table III depicts the augmentation technique used in this study.
Figure 5 shows the in-place augmentation process [28]. The process of applying
in-place data augmentation is [28]:

Step-1: At first, augmentation parameters including zoom_range,
brightness_range, fill_mode, and horizontal_flip are defined.

Step-2: An instance of an ImageDataGenerator class, specifying the
augmentation parameters is also created.

Step-3: An original batch of images is passed to the ImageDataGenerator class.

Step-4: Then, the batches of augmented data are generated and returned to the
calling method.

Step-5: After that, the model is trained on batches of images.

Figure 5: Image Augmentation (in-place ) process. TABLE III: Augmentation technique used in this study. Generator Type | Facility
---|---
Zoom Range | 0.99-1.01
Brightness Range | 0.8-1.2
Fill Mode | Constant
Horizontal Flip | True

####  III-B6 Data Visualization:

This stage requires sampling and reviewing the input data to observe the
randomly transformed images and to verify that the transformations are being
applied as expected. This stage is also used to confirm the readability of the
input images. ImageDataGenerator library is used to generate a batch of
randomly transformed images to visualize the transformed images. These
transformed images are then visualized using the visualization library
Matplotlib via 2D representation with the new picture dimensions [25]. Figure
6 shows the visualization of the transformed image sample of our dataset.

###  III-C Splitting Dataset

The dataset is split into two separate sets for the purpose of model training
and testing, ensuring that the model is trained on a portion of the data and
evaluated on a separate portion.

  * •

Train data

  * •

Test data

Here, we have used 90% (2872) of data for training the model and 10% of data
for testing the validation of the model. Traying several splitting ratios, we
ended up with the splitting ratios 90% and 10% because model gives the better
training accuracy and validation accuracy and also gives satisfactory results
on other performance metrics for this splitting ratio.

Figure 6: Random transformed image samples from the dataset.

###  III-D Model Building

Monkeypox and non-monkeypox lesions can be classified using pre-trained models
that were designed to identify 1000 classes in Imagenet. In this study, we
have used three pre-trained including DenseNet121, ResNet50, and VGG16.
Nevertheless, these pre-trained architectures have a lot of convolutional
layers and take a lot of computation time and storage space. So, we proposed a
lightweight convolutional network model named after ’MpoxSLDNet’ to address
these problems. This DL network ’MpoxSLDNet’ uses a classification-based
detection approach to extract relevant information from an input image, and
improves it by adding layers (convolution, pooling, and dense). Table
LABEL:Table6 shows the summary of models used in this study. The overall
workflow to train the pre-trained model and MpoxSLDNet model are shown in
Figure 7

TABLE IV: Summary of models used in this study. | Model
---
Name
Description |  | Batch
---
Size
Epochs | Optimizer |  | Loss
---
Function
MpoxSLDNet |  | A custom-built CNN model designed for
---
the identification of Monkeypox and
Non-Monkeypox lesions.
32 | 20 | Adam |  | Binary
---
Cross entropy
DenseNet121 |  | A pre-trained CNN model with 121 layers
---
was used for feature extraction and
classification.
32 | 20 | Adam |  | Binary
---
Cross entropy
ResNet50 |  | A pre-trained CNN model with 50 layers
---
is employed to extract features and perform
classification tasks.
32 | 20 | Adam |  | Binary
---
Cross entropy
VGG16 |  | A pre-trained CNN model with 16 layers is
---
employed to extract features and perform
classification tasks.
32 | 20 | Adam |  | Binary
---
Cross entropy

####  III-D1 Architecture of our Proposed MpoxSLDNet model :

Figure 8 shows the structure of the MpoxSLDNet model. This section provides a
detailed description of the proposed (MpoxSLDNet) model’s structure.

Figure 7: Pipeline to train the pre-trained model and MpoxSLDNet model.

Our proposed model is a CNN architecture designed for 2-dimensional (2-D)
image analysis that consists of six convolutional layers and six max-pooling
layers. Input images with 224×224 dimensions are passed into the input layer.
The initial convolutional layer consists of 16 feature kernel filters with a
filter size of 3x3 and the padding is set to ’same’. The second convolutional
layer consists of 32 feature kernel filters and filter size is 3×3 and
padding=”same”. The third convolutional layer with a kernel size of 3×3 uses
64 feature maps. The fourth convolutional layer with a kernel size of 3×3 and
use 128 feature maps. The fifth convolutional layer with a kernel size of 3×3
and use 256 feature maps. The next six convolutional layers with kernel size
3×3. These convolutional layers have 512 nodes. The resulting output
dimensions of convolutional layers are 7x7x512. The ReLU activation function
is applied after each of these convolutional layers to introduce non-linearity
into the model which is important for learning complex features and patterns
in the input data. The eqn 1 for the ReLu activation function.

$Y(x)=\begin{cases}x,\hskip 14.22636ptifx\geq 0\\\ 0,\hskip
14.22636ptOtherwise\end{cases}$ (1)

The convolutional layer performs the extraction of significant features from
the input images, capturing valuable patterns and information. Here, The
convolution operation can be mathematically expressed via eqn 2.

${Y_{i,j}}=\sum_{s=1}^{S}\sum_{t=1}^{T}{W_{s,t}}{X_{(i+s-1),(j+t-1)}}$ (2)

Output shape of the convolutional layer can be calculated by the eqn 3.

$\begin{split}output\\_shape=&((input\\_shape-filter\\_size+2\\\
&*padding)/stride)+1\end{split}$ (3)

Consider a 5×5 image that convolved with a 3×3 filter where padding is zero;
stride is one. The convolution operation is shown in Figure 9. In the
MpoxSLDNet model, After each conv2D layer, a BatchNormalization layer is
utilized to normalize the output of the convolutional layer and to reduce
overfitting. In this model, after each pair of conv2D and BatchNormalization
layer MaxPool2D layer of a 2x2 filter and a stride of 2 is applied which
downsamples the feature map by taking the maximum value within each pooling
window, thereby reducing the dimensionality of the data. Here, the default
parameters of padding for the MaxPool2D layer are valid. The max pooling
operation can be represented mathematically by the eqn 4.

${Y_{i,j}}=max_{s=1}^{S}max_{t=1}^{T}{X_{(i+s-1),(j+t-1)}}$ (4)

The shape of the output from the max pooling layer can be determined using eqn
5.

Figure 8: Architecture of proposed (MpoxSLDNet) model.

$\begin{split}output\\_shape=&((input\\_shape-pool\\_size)\\\
&/strides)+1\end{split}$ (5)

The Figure 10 provides an example of the max pooling operation using a 2x2
pixel filter on a 4x4 pixel input. After six convolution layers and six max-
pooling layers, a flattened layer is added that flattens the output of the
preceding layer into a one-dimensional array, facilitating its transfer to a
fully connected layer because a fully associated layer accepts a 1D vector as
input while convolution and pooling layer’s output is typically a three-
dimensional tensor with dimensions representing the height, width, and number
of channels. Figure 11 shows process of converting 1D array with the flatten
layer. Then the converted 1D array is utilized by three fully connected layers
of 256 units, 128 units, and 64 units, respectively, which are activated by
the ReLu activation function. A Batch Normalization layer and dropout function
are applied individually after each dense layer. A mathematical representation
for a fully connected layer can be given as eqn 6.

$Y=f(Wx+b)$ (6)

Figure 9: Example of the Convolution operation.

Figure 12 shows the graphical representation of fully connected layer. Then
the last layer is 2 units dense layer with a sigmoid activation function
producing a binary classification output. Table IV shows the summary of
hyperparameters used in the proposed MpoxSLDNet model.

TABLE V: Hyper parameters for the proposed MpoxSLDNet model. Hyper Parameter | Description
---|---
No. Of Convolutional Layer | 6
No. Of Max pooling Layer | 6
Activation Function | ReLu, Sigmoid
Batch Size | 32
Optimizer | Adam
Loss Function | Binary Cross entropy
Figure 10: Illustration of Max Pooling. Figure 11: Converting 1D array. Figure
12: Fully Connected Layer.

##  IV Result Analysis

In this study, the results obtained from training and testing all models on
the dataset are presented. The performance of the models using various
assessment measures, including confusion matrices, AUC-ROC score,
Classification report, precision, recall, as well as F1-score is evaluated.
Furthermore, a detailed discussion of the results and the strengths and
weaknesses of each model in the context of monkeypox lesion detection are also
provided. The original classifier of pre-trained architectures has been
replaced. As a result of Kaggle’s limited number of images, in-place data
augmentation techniques were implemented during training to expand the dataset
and solve the overfitting problem. After completing 20 training epochs, we
recorded the accuracy and loss metrics for each model.

###  IV-A Assessing the MpoxSLDNet model’s performance through multiple runs

Table VI shows the training and validation accuracy obtained from running the
proposed model three times for 20 epochs. The purpose of multiple runs is to
mitigate the effects of randomness during the training phase and provide a
more reliable evaluation of how well the model performs. Based on the Table
VI, the MpoxSLDNet model attained an average training accuracy of 96.89% and
an average validation accuracy of 93.08%. While the performance of the model
is consistent across the three runs, there is some variation in the accuracy
values. This highlights the importance of evaluating the model’s performance
multiple times to obtain a more accurate estimate of its accuracy.

###  IV-B Accuracy and Loss Analysis

The validation accuracies, training accuracies, and losses of each model are
shown in Table VII. Our proposed model (MpoxSLDNet) exhibited a training
accuracy of 97.53% and a training loss of 0.0862. The validation phase
resulted in an accuracy of 94.56% and a loss of 0.1583 for the MpoxSLDNet
model. Similarly, the training phase for DenseNet121 resulted in an accuracy
of 85.86% and a loss of 0.3443. DenseNet121 achieved a validation accuracy of
84.38% with a validation loss of 0.3280. Moving on to ResNet50, the training
accuracy and loss were recorded as 72.42% and 0.5333, respectively. The
validation phase for ResNet50 resulted in an accuracy of 67.19% and a loss of
0.5602. Similarly, The training accuracy for VGG16 was recorded as 89.45%,
with a corresponding training loss of 0.2404. VGG16 achieved a validation
accuracy of 86.25% with a validation loss of 0.3080. In summary, the
MpoxSLDNet models that we proposed demonstrated superior performance in terms
of both training and validation accuracy, outperforming other models such as
DenseNet121, ResNet50, and VGG16.

TABLE VI: Multiple Training accuracy and Testing accuracy for the MpoxSLDNet
model.

Experiment |  Epoch |  Training Accuracy |  Loss |  Testing Accuracy |  Loss
---|---|---|---|---|---
1 |  20 |  97.60% |  0.0826 |  93.12% |  0.2199
2 |  20 |  95.54% |  0.1300 |  91.56% |  0.2745
3 |  20 |  97.53% |  0.0862 |  94.56% |  0.1583

TABLE VII:  Evaluation of accuracy and loss for each model during training and
validation.

Model Name |  Training Accuracy |  Loss |  Testing Accuracy |  Loss
---|---|---|---|---
DenseNet121 |  85.86% |  0.3443 |  84.38% |  0.3280
ResNet50 |  72.42% |  0.5333 |  67.19% |  0.5602
VGG16 |  89.45% |  0.2404 |  86.25% |  0.3080
Our proposed Model (MpoxSLDNet) |  97.53% |  0.0862 |  94.56% |  0.1583

Figure 13: Representation of accuracy curve of our proposed (MpoxSLDNet)
model. Figure 14: Representation of loss curve of our proposed (MpoxSLDNet)
model.

Figure 13 as well as Figure 14 shows the accuracy and loss learning curve of
our proposed model (MpoxSLDNet). The learning curve illustrates the evolution
of the model’s performance changes with training. Learning curves provide
valuable insights into how the model behaves and help us fine-tune the
hyperparameters for optimal performance, as well as identify the model’s
generalization capability, indicating how well it adapts to unseen data or
performs well. From the accuracy as well as loss curve, we can observe that
the validation and accuracy curves start to converge as the training goes on,
showing that the model is learning and improving its performance and whether
it is overfitting or underfitting the data. By analyzing the learning curve,
we can make informed decisions that the model is starting to generalize well
on the unseen data, which is a good sign of model performance.

TABLE VIII: Comparative Classification performance analysis of our proposed
model vs. pre-trained model.

Model Name |  Patient Status |  Precision |  Recall |  F1-score
---|---|---|---|---
DenseNet121 |  Monkeypox(0) |  0.88 |  0.80 |  0.84
Non-Monkeypox(1) |  0.81 |  0.89 |  0.85
ResNet50 |  Monkeypox(0) |  0.86 |  0.42 |  0.56
Non-Monkeypox(1) |  0.61 |  0.93 |  0.74
VGG16 |  Monkeypox(0) |  0.94 |  0.78 |  0.85
Non-Monkeypox(1) |  0.81 |  0.95 |  0.87
MpoxSLDNet model |  Monkeypox(0) |  0.94 |  0.94 |  0.94
Non-Monkeypox(1) |  0.94 |  0.94 |  0.94

###  IV-C Classification Report Analysis

The classification report gives a detailed breakdown of precision, recall, and
F1 scores of each class. Using the classification report, we looked at
precision, recall, and F1-score for each class to assess the proposed
MpoxSLDNet model’s performance. Table VIII presents the classification report
for each models used in this study, including DenseNet121, ResNet50, VGG16,
and our proposed model (MpoxSLDNet). The classification report includes
precision, recall, and F1-score values for each class, namely Monkeypox (0)
and Non-Monkeypox (1), indicating the model’s performance in detecting
monkeypox lesions. For DenseNet121, the precision for Monkeypox is 0.88,
indicating that 88% of the samples classified as Monkeypox were correctly
identified. The recall for Monkeypox is 0.80, indicating that 80% of the
actual Monkeypox samples were correctly identified. The F1-score for Monkeypox
is 0.84, providing a balanced measure of precision and recall. Similarly, for
Non-Monkeypox class, the precision, recall, and F1-score values are provided.
The same evaluation metrics are provided for ResNet50 and VGG16 models,
highlighting their performance in detecting Monkeypox and Non-Monkeypox
lesions. Our proposed model, MpoxSLDNet, demonstrates superior performance in
both classes, with high precision, recall, and F1-score values of 0.94 for
both the Monkeypox and Non-Monkeypox classes. This indicates that the
MpoxSLDNet model achieved a high level of accuracy in correctly identifying
both types of lesions. This classification report table allows for a
comprehensive comparison of the models’ performance in detecting monkeypox
lesions, providing insights into the precision, recall, and F1-score values
for each class. These metrics help to evaluate and select the most effective
model for accurate monkeypox lesion detection.

Figure 15: Confusion Matrix for our proposed (MpoxSLDNet) model. Figure 16:
AUC-ROC curve for all models.

###  IV-D Confusion Matrix and AUC ROC Curve Analysis

Confusion matrices are good tools for evaluating classification models. This
confusion matrix shows how many correctly classified and misclassified samples
there are for each class. Figure 15 shows the confusion matrix of our proposed
MpoxSLDNet model. Here, 320 of the test images are evaluated. True Positive
tells us how often the model correctly categorizes monkeypox. In the same way,
True Negative denotes the model’s ability to correctly classify non-monkeypox
as non-monkeypox. From Figure 15, we can observe that the MpoxSLDNet model
accurately identifies 152 images as monkeypox (True positive) and 149 images
as non-monkeypox (True Negative). According to Figure 15, we can conclude that
our model recognized 301 images correctly out of 320. On the other hand, false
positive means that the model incorrectly classifies non-monkeypox as
monkeypox. Conversely, False negative shows the instances where the model
mistakenly classifies monkeypox as non-monkeypox. We can also observe that 9
images of Non-Monkeypox were incorrectly classified as monkeypox
positive(False Positive), and 10 images of Monkeypox were incorrectly
classified as Non-Monkeypox(negative). It shows that our approach can
accurately identify Monkeypox patients, with high true positives and true
negatives. Additionally, we employed Receiver Operating Characteristic (ROC)
curves to evaluate the classification performance which represents the true
positive rate (TPR) plotted against the false positive rate (FPR), aiding in
the detection of positive Monkeypox cases within the tested Monkeypox and Non-
Monkeypox images. Here, AUC measures how much our model is capable of
distinguishing between our two classes, Monkeypox and Non-Monkeypox. The AUC-
ROC curve is displayed for all the employed models in Figure 16. According to
the data provided in Table IX, the proposed model demonstrated an AUC-ROC
score of 0.94. The AUC-ROC score of the DenseNet121, ResNet50, and VGG16 are
0.84, 0.68, and 0.86, respectively. It is observed that the best AUC-ROC score
belongs to the MpoxSLDNet model. Hence, the MpoxSLDNet model is relatively
effective at distinguishing between positive and negative instances.

###  IV-E Overall Performance Evaluation and Comparative Analysis: Proposed
Model vs. Pre-trained Models

In this section, we present an overall comprehensive analysis and comparison
of the performance between our proposed model, MpoxSLDNet, and pre-trained
models. The evaluation is conducted using key metrics including accuracy,
precision, recall, and F1-score, AUC score. As shown in Table IX, the highest
precision score of 0.94 occupied by the MpoxSLDNet model indicates that the
model is good at correctly identifying positive instances. On the other hand,
as recall increases, monkeypox images are identified more frequently, and the
opposite is also true. With a recall score of 0.94, the MpoxSLDNet model
reflects its ability to correctly identify a significant number of monkeypox
images. Apart from this, the F1 score combines precision and recall into one
score. The F1-score achieves its highest value of 1, indicating excellent
performance, and its lowest value of 0, indicating poor performance. The
MpoxSLDNet model achieves the highest average F1 score of 0.94, indicating a
superior balance between precision and recall compared to the pre-trained
model. Furthermore, the accuracy metric measures how many instances are
classified correctly. With an accuracy score of 0.94, the model was able to
classify instances correctly. Figure 17 serves as a succinct overview of the
model’s performance, allowing for easy comparison and identification of the
top-performing model according to the selected metrics. The x-axis represents
the metrics name as well as y-axis represents the corresponding values for
precision, recall, F1-score, accuracy, and AUC score ranging from 0 to 1. The
graph provides a visual representation of how each model performed in terms of
these metrics. Higher values indicate better performance. Undoubtedly,
MpoxSLDNet consistently demonstrates superior precision, recall, F1-score, and
accuracy compared to the other models. This suggests that the MpoxSLDNet model
surpasses the other models in accurately detecting monkeypox lesions.

Figure 17: Overall performance Comparison of MpoxSLDNet model vs. pre-trained
model.

###  IV-F Evaluating the Performance of the Proposed Model in Comparison to
Existing Research

Table X presents a comparative analysis between the existing study and the
proposed system, both utilizing the Monkeypox Skin Lesion Dataset (MSLD). The
accuracy scores, represented as percentages, reflect the models’ ability to
accurately classify monkeypox lesions from non-monkeypox lesions. Our proposed
MpoxSLDNet model demonstrates an accuracy score of 94.56%, surpassing all
other models in accuracy. In comparison, the previously employed models
exhibited accuracy scores ranging from 71.86% to 91.11%. Apart from, in this
study, other pre-trained models like DenseNet121, ResNet50, and VGG16
demonstrate accuracy values ranging from 67.19% to 86.25%. This also
highlights the superior accuracy achieved by MpoxSLDNet compared to the other
models, showcasing its effectiveness in accurately classifying monkeypox
lesions. Table X effectively summarizes the accuracy performance of the
different models, highlighting the superior performance of MpoxSLDNet in
accurately detecting and classifying monkeypox lesions.

TABLE IX: Overall performance of our proposed model vs. pre-trained model.

Model Name |  Precision |  Recall |  F1-score |  Accuracy |  AUC-ROC Value
---|---|---|---|---|---
DenseNet121 |  0.85 |  0.84 |  0.84 |  0.84 |  0.84
ResNet50 |  0.74 |  0.67 |  0.65 |  0.67 |  0.68
VGG16 |  0.87 |  0.86 |  0.86 |  0.86 |  0.86
Our proposed Model (MpoxSLDNet) |  0.94 |  0.94 |  0.94 |  0.94 |  0.94

TABLE X: Result Analysis of existing study with the proposed system. Model Name | Accuracy
---|---
MobileNetv2 [1] | 91.11%
VGG16 [11] | 81.48%
ResNet50 [11] | 74.07%
InceptionV3 [11] | 82.96%
Ensemble [11] | 79.26%
VGG19-CBAM-Dense [20] | 71.86%
DenseNet121-CBAM-Dense [20] | 78.27%
EfficientNetB3-CBAM-Dense [20] | 81.43%
MobileNetV2-CBAM-Dense [20] | 74.07%
Xception-CBAM-Dense [20] | 83.89%
Proposed Model (MpoxSLDNet) | 94.56%
VGG16 | 86.25%
DenseNet121 | 84.38%
ResNet50 | 67.19%

##  V Discussion

The study’s outcomes highlight the substantial progress made in monkeypox
lesion detection with the development and evaluation of the MpoxSLDNet model.
By focusing on key performance metrics like accuracy, precision, recall, and
F1-score, our results consistently demonstrate the MpoxSLDNet model’s
superiority over traditional pre-trained architectures such as DenseNet121,
ResNet50, and VGG16. Through rigorous validation and multiple runs, our model
consistently achieves high training and validation accuracies, indicative of
its robustness and reliability in discerning monkeypox lesions from digitized
skin lesion images. Additionally, the use of confusion matrices and AUC-ROC
curves further validates the model’s efficacy in distinguishing between
positive and negative instances, with the MpoxSLDNet model consistently
outperforming others, as evidenced by its highest AUC-ROC score. These
findings hold significant implications for disease management, providing
healthcare practitioners with a powerful tool for early detection and
intervention in monkeypox cases, thereby enabling prompt treatment and
containment measures to curb disease transmission. So we can say, MpoxSLDNet
model represents a groundbreaking advancement in the realm of monkeypox lesion
detection, offering versatile applications with profound potential impact.
Primarily, it enables early detection and diagnosis of monkeypox lesions,
aiding in timely intervention and treatment to mitigate disease progression
and transmission. Its non-invasive nature makes it particularly advantageous
in healthcare settings, minimizing physical contact and reducing the risk of
disease spread among healthcare professionals and patients. By providing
automated lesion detection and classification, the model enhances clinical
decision-making, improving diagnostic accuracy and treatment outcomes.
Furthermore, its integration into public health surveillance systems holds
promise for early outbreak detection, epidemiological monitoring, and targeted
intervention strategies. Beyond healthcare, the MpoxSLDNet model may influence
industries involved in disease surveillance, pharmaceutical research, and
public health policy, informing efforts towards developing treatments,
vaccines, and policy decisions for disease control and prevention. Overall,
the MpoxSLDNet model’s deployment has the potential to significantly enhance
healthcare delivery, disease control efforts, and public health outcomes on a
global scale.

##  VI Conclusion

In conclusion, this study introduced a deep learning-based framework for
effectively classifying skin lesions associated with monkeypox and non-
monkeypox that achieved promising results. Through comprehensive
experimentation and comparison with established pre-trained models, including
VGG16, ResNet50, and DenseNet121, our research aimed to evaluate MpoxSLDNet’s
effectiveness. The findings demonstrate MpoxSLDNet’s superior performance
metrics, including accuracy, precision, recall, F1-score, and AUC,
underscoring its potential as a reliable tool for early detection and
categorization of monkeypox lesions. Table VIII demonstrates that the
suggested MpoxSLDNet model showed superior performance compared to traditional
pre-trained models, with higher accuracy, precision, recall, F1-score, and AUC
metrics. The proposed MpoxSLDNet model achieved remarkable results, including
an accuracy rate of 94.56%, a precision score of 0.94, a recall rate of 0.94,
an F1-score of 0.94, and an AUC value of 0.94. In comparison, VGG16,
DenseNet121, and ResNet50 obtained accuracy scores of 86.25%, 84.38%, and
67.19% respectively.Despite these promising outcomes, it is crucial to
acknowledge inherent limitations in our study, particularly concerning the
dataset’s representativeness of real-world clinical scenarios. Future research
efforts should prioritize expanding the dataset to include a more diverse
range of lesion variations and conducting rigorous clinical validation
studies.

##  VII Limitation and Future Work

Despite the promising outcomes achieved with the Mpox- SLDNet model, it is
important to acknowledge certain limitations inherent in our study. The
dataset used for training and evaluation, while carefully curated, may not
fully represent the diverse range of lesion variations encountered in real-
world clinical settings. This limitation could potentially impact the model’s
ability to generalize to unseen cases, particularly those with rare or
atypical lesion characteristics. To address these limitations and further
enhance the MpoxSLDNet model’s effectiveness, expanding the dataset to include
a more diverse range of lesion types, sizes, colors, and textures is
imperative to improve the model’s robustness and generalizability across
different patient populations and clinical contexts. Additionally, conducting
rigorous clinical validation studies to assess the model’s performance in
real-world settings is crucial.

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