# Transfer learning and Local interpretable model agnostic based visual
approach in Monkeypox Disease Detection and Classification: A Deep Learning
insights

Md Manjurul Ahsan
Department of Industrial and Systems Engineering
University of Oklahoma
Norman, Oklahoma-73071
<EMAIL_ADDRESS>
&Tareque Abu Abdullah
Department of Computer Science
Lamar University
Beaumont, TX-77710
<EMAIL_ADDRESS>
&Md Shahin Ali
Department of Biomedical Engineering
Islamic University
Kushtia-7003, Bangladesh
<EMAIL_ADDRESS>
&Fatematuj Jahora
Child Development and Social Relationship
Government College of Applied Human Science
Azimpur, Dhaka-1205, Bangladesh
<EMAIL_ADDRESS>
&Md Khairul Islam
Department of Biomedical Engineering
Islamic University
Kushtia-7003, Bangladesh
<EMAIL_ADDRESS>
&Amin G. Alhashim
Department of Industrial and systems Engineering
Arizona State University
7271 E. Sonoran Arroyo Mall Mesa, AZ-85212
<EMAIL_ADDRESS>
&Kishor Datta Gupta
Department of Computer and Information Science
Clark Atlanta University
Atlanta, GA-30314, USA
<EMAIL_ADDRESS>
###### Abstract

The recent development of Monkeypox disease among various nations poses a
global pandemic threat when the world is still fighting Coronavirus
Disease-2019 (COVID-19). At its dawn, the slow and steady transmission of
Monkeypox disease among individuals needs to be addressed seriously. Over the
years, Deep learning (DL) based disease prediction has demonstrated true
potential by providing early, cheap, and affordable diagnosis facilities.
Considering this opportunity, we have conducted two studies where we modified
and tested six distinct deep learning models—VGG16, InceptionResNetV2,
ResNet50, ResNet101, MobileNetV2, and VGG19–using transfer learning
approaches. Our preliminary computational results show that the proposed
modified InceptionResNetV2 and MobileNetV2 models perform best by achieving an
accuracy ranging from 93% to 99%. Our findings are reinforced by recent
academic work that demonstrates improved performance in constructing multiple
disease diagnosis models using transfer learning approaches. Lastly, we
further explain our model prediction using Local Interpretable Model-Agnostic
Explanations (LIME), which plays an essential role in identifying important
features that characterize the onset of Monkeypox disease.

_K_ eywords Convolutional neural network $\cdot$ Deep learning $\cdot$ Disease
diagnosis $\cdot$ Image processing $\cdot$ Monkeypox $\cdot$ Transfer learning

##  1 Introduction

Monkeypox, also known as Monkeypox Virus (MPXV) disease, is caused by
infection with the virus of the same name and is usually found in monkeys [1].
Monkeypox usually occurs in Africa, Central and West Africa, and Asia [2].
Although it can infect any mammal, the virus spreads to humans primarily
through the bite of an infected animal like a bat or primate such as a monkey
[3]. There are several early monkeypox symptoms, including muscle pain,
headache, fatigue, and fever. The disease almost resembled chickenpox,
smallpox, and measles. It can be distinguished from the appearance of swollen
glands, usually present behind the ear, below the jaw, neck, or groin, before
the onset of the rash [4]. Even though the virus is not life-threatening, it
causes complications in severe cases, including sepsis, pneumonia, and loss of
eye vision [5].

Although monkeypox does not affect humans very often, the slight chance of
affecting it should still make people think twice about their exposure to
monkeys and rodents — especially in certain parts of the world where outbreaks
of this disease happen more often. According to the Centers for Disease
Control and Prevention (CDC), a new strain of monkeypox disease will infect
millions globally in the coming years [6].

Monkeypox was initially found in 1958 and was rediscovered in the Republic of
Congo in West Africa in 2014 [7]. While it may not be as well-known as the
likes of Ebola or Zika, the monkeypox virus has the potential to become just
as large of a global health threat as those two if enough is not done to stop
it from spreading further. Recently, the virus has been slowly spreading, and
the number of Monkeypox patients is increasing day by day. According to CDC,
as of June 06, 2022, the virus had spread among 29 countries, and the number
of Confirmed cases was around 1029 [8]. Currently, there are no appropriate
treatments available for Monkeypox disease [9, 10]. However, two oral drugs,
Brincidofovir and Tecovirimat, are recommended for urgent treatment, mainly
used to treat the smallpox virus. Vaccination is the ultimate solution to
prevent this disease [11].

The symptoms of Monkeypox, smallpox, and measles are almost similar making it
difficult to identify without a proper laboratory test [12]. The definitive
way to diagnose the virus is to use electronic microscopy and test the skin
lesions. In addition, the virus can be identified using Polymerase chain
reaction (PCR) testing, which is heavily used to test for COVID-19 [13]. PCR
is usually used for laboratory tests. However, during the onset of COVID-19,
we have seen that the PCR test kit cannot correctly diagnose COVID-19 patients
around 40% of the time [14] which means that multiple tests are required to
increase accuracy. As an effect, if Monkeypox becomes another global pandemic,
then it will be hard to facilitate enough tool kits to test both Monkeypox and
COVID-19 patients. Additionally, many developed nations cannot afford such an
expensive tool kit to use regularly [15].

In recent past Machine learning (ML) demonstrated promising results in medical
imaging and disease diagnosis [16] which means that diseases such as cancer,
pneumonia, and COVID-19 can be detected without doctor intervention [17]. As
Monkeypox symptoms significantly affect the human skin, images containing
infected human skin can be used to develop ML-based diagnosis model [9].

Monkeypox disease detection using any ML or deep learning (DL) approach is not
available at the time of writing. However, over the year, several studies
demonstrated that DL-based models can be a reliable option to detect
Chickenpox and Measles disease, which have almost similar symptoms as
Monkeypox [18, 19, 20]. For example, Chae et al. (2018) employed a very deep
neural network (DNN) and long-short term memory (LSTM) model to identify
chickenpox, which outperforms the classic autoregressive integrated moving
average (ARIMA) model [18]. Arias et al. (2020) developed a deep learning
model to detect varicella-zoster, a virus that can cause damage to the eyes.
Their proposed model can identify the virus up to 97% accuracy. Bhadula et al.
(2019) employed convolutional neural networks (CNN) to detect skin disorders.
The authors use the CNN model to detect acne and lichen planus and achieved
96% and 92% accuracy, respectively [20]. Sriwong et al. (2019) utilized the
CNN approach to detect skin diseases and achieved 79.2% accuracy. Skin
diseases such as actinic keratoses, basal cells, and benign keratosis are some
of the skin diseases that the authors wanted to detect throughout the study
[21].

Based on the referenced literature, it can be infer that, DL techniques have
been used extensively to detect various skin diseases caused mainly by various
infections. Therefore, traditional CNN is a good approach for developing deep
learning-based models to diagnose the Monkeypox disease diagnosis. In this
work, we proposed and tested six distinct improved deep CNN models by adopting
models, namely, VGG16 [22], InceptionResNetV2 [23], ResNet50 [24, 25, 26],
ResNet101 [27], MobileNetV2 [28], and VGG19 [22], using transfer learning
approaches [29]. The following is a summary of our contribution:

  1. 1.

Improvement and assessment of six distinct deep learning models (VGG16,
InceptionResNetV2, ResNet50, ResNet101, MobileNetV2, and VGG19) for detecting
Monkeypox disease using image data.

  2. 2.

Verify and interpret the model’s performance employing Local Interpretable
Model Agnostic Explanations (LIME).

##  2 Methodology

This study aims to detect Monkeypox disease by developing CNN model using
transfer learning approaches. In this work, we have used pre-trained deep
learning architectures to extract essential features that are practically
difficult to initially identify by visual inspection due to their similarities
with other infectious diseases such as chickenpox and measles. We then fed our
data through several layers, where the top most dense layer is used to detect
the Monkeypox disease.

###  2.1 Dataset

The traditional approach of deep learning demands a significant amount of
input data in order to train. The recent advancement of transfer learning
approaches, on the other hand, demonstrates that a limited dataset could be
used to train and develop a robust CNN model that can efficiently perform
during the predictions [14]. At the time of writing, only one publicly
available dataset exists, which is obtained from the Kaggle repository. The
assignment of the dataset that has been used during this study is presented in
Table 1. Our assessment of the data size differs for two different studies.
For Study One, we have used 76 samples (43 Monkeypox and 33 Non-Monkeypox
images), whereas, for Study Two, 818 samples are used, which contains 587
Monkeypox and 231 Normal images.

Table 1: Monkeypox datasets used in this study. Dataset | Label | Train set | Test set | Total
---|---|---|---|---
Study One | Monkeypox | 34 | 9 | 76
Normal | 26 | 7
Study Two | Monkeypox | 469 | 118 | 818
Normal | 185 | 46

Figure 1 presents a set of representative images of both Monkeypox and Non-
Monkeypox individuals from the aforementioned datasets.

Figure 1: Images retrieved from open source data repositories to use in our
proposed research.

###  2.2 Data Augmentation

Data augmentation is a technique for increasing the size of a training dataset
without acquiring new data from existing training data [30]. Many computer
vision applications have benefited from Deep CNN, which is sensitive to large
amounts of data to reduce overfitting. An overfitting network detects a
function with a significant variance to estimate the training dataset
accurately. Affine transformations, deformations, or geometric distortions are
often used to enrich data and enlarge sample sizes for deep neural networks
[31]. We have applied data augmentation to reduce the danger of overfitting,
which involves random image alterations such as flipping, rotation height and
weight shift, shearing, and zooming.

Flipping: Flipping is performed on each image inside the training set.
Vertical flips capture a distinctive property of medical images: reflection
invariance in the vertical direction. Vertical inversions may not always
correlate to images that are input. For input images, only horizontal flips of
the source images are used. In contrast, a vertical mass flip would still
result in a considerable weight.

Scaling: Each I is scaled in either the x or y-axis; more precisely, affine
transformation is used. $A$ = $\begin{pmatrix}s_{x}&0\\\ 0&s_{y}\end{pmatrix}$

Rotations: Rotations are performed using the affine transformation as well,
$A$ = $\begin{pmatrix}cos\theta&-sin\theta\\\
sin\theta&cos\theta\end{pmatrix}$ , where $\theta$ is a value between $0$ and
$360$ degrees.

Shearing: Shear angle in degrees in a counter-clockwise direction scaled by
0.2 which is represented by the following affine transformation, $A$ =
$\begin{pmatrix}1&s\\\ 0&1\end{pmatrix}$. $s$ defines the amount that I is
sheared, and it is in the range of [0, 0.2].

Translation: Simply moving the image on the X or Y axis is required for
translation (or both). For example, it might be assumed that the image seems
to have a black background outside its boundaries and translate the image
accordingly. This enhancement can discover the vast majority of things
anywhere inside the image. Thus, CNN is compelled to examine everywhere.

###  2.3 Transfer learning approaches

Recent research demonstrates the outstanding performance of deep CNN for the
classification of image data with a high accuracy rate [14]. The development
of deep CNN-based models that have established themselves as a promising tool
for image classification and computer vision is gaining popularity day by day
[32, 33]. Traditional CNN requires large amounts of data, whereas transfer
learning can be applied to develop a model that can be adopted for other tasks
[14]. During this study, the internal structure of the existing models was
kept same to extract the important features from the Monkeypox images without
further increasing any CNN-related complexity. Then the top most layers are
modified to perform the classification. Throughout the course of this
research, we modified six distinct deep-learning models, namely, VGG16,
InceptionResNetV2, ResNet50, ResNet101, MobileNetV2, and VGG19.

Figure 2: Modified layer

Figure 3 displays the flow diagram of the proposed models used during this
study.

Figure 3: Flow diagram of the proposed models.

###  2.4 Proposed architecture

A pre-trained network is one that has been trained on a large dataset and can
be applied to other datasets with comparable properties. A prime example is
VGG16 networks, which have been adopted by many recent literatures to detect
COVID-19 during the onset of the COVID-19 pandemic [14, 34]. Figure 4 shows a
sample modified architecture that has been proposed during this study for
VGG16.

Figure 4: Updated VGG16 network implemented during this experiment.

The modified architecture generally follows the following three steps:

  1. 1.

The model is initially initialized with a pre-trained network without FC
layers.

  2. 2.

Then a new FC layer is added with "Softmax" activation function and added to
the top of the actual VGG16 model.

  3. 3.

Finally, during the training phase, the weight of the convolution is frozen so
that the FC layer can be developed during the experiment.

Similar procedure is followed to develop every transfer learning model used
during this study. In this study, the primary modification of the model for
all CNN architecture is developed as shown in Figure 2.

Our proposed study optimized three parameters during the training phase: batch
size, epochs, and learning rate. Manually tunning that parameter is time-
consuming; therefore, we have used the grid search method to find the best
results. Table 2 summarizes the initial and optimal parameters found during
the experiment.

Table 2: Parameter settings used during this study.

Parameters | Study One | Study Two
---|---|---
Initial parameter | Optimize parameters | Initial parameters | Optimize parameters
Batch size | 5, 10, 20, 40 | 10 | 10, 20, 30, 40 | 30
Epochs | 20, 30, 40, 50 | 50 | 30, 40, 50, 70 | 50
Learning rate | .001, .01, 0.1 | .001 | .001, .01, 0.1 | .001

###  2.5 Experiment setup

Adaptive momentum, also known as Adam, is employed to optimize the performance
of our proposed model due to the fact that it has a reliable performance with
regard to binary image classification [35]. The experiment was carried out
using an office-grade laptop with a configuration of Windows 10, Core I7, and
with 16 GB RAM facilities. The dataset is split into the following ratios,
which is often common in machine learning domains—train set: test set= 80:20.
The overall experimentation is run five times, and the final result is
presented by averaging all five results. To evaluate the model’s performance
following statistical measurement is used [32]:

$Accuracy=\frac{T_{p}+T_{n}}{T_{p}+T_{n}+F_{p}+F_{n}}$ (1)
$Precision=\frac{T_{p}}{T_{p}+F_{p}}$ (2) $Recall=\frac{T_{p}}{T_{n}+F_{p}}$
(3) $F1-score=2\times\frac{\textrm{Precision}\times\textrm{
Recall}}{\textrm{Precision~{}+~{}Recall}}$ (4)
$Sensitivity=\frac{T_{p}}{T_{p}+F_{n}}$ (5)
$Specificity=\frac{T_{n}}{T_{n}+F_{p}}$ (6)

Here, $T_{p}$ (True Positive) = Monkeypox infected individual identified as
Monkeypox

$T_{n}$ (True Negative) = Monkeypox infected individual identified as non-
Monkeypox

$F_{p}$ (False Positive) = Non-Monkeypox individual identified as Monkeypox

$F_{n}$ (False Negative) = Monkeypox individual identified as non-Monkeypox.

##  3 Results

###  3.1 Study One

Equations 1, 2, 3, 4, 5 and 6 were used in Study One to investigate each and
every CNN models performance on both the train and test sets. In this
particular scenario, InceptionResNetV2 and MobileNetV2 performed far better
than any other models in terms of accuracy, precision, recall, F-1 score,
sensitivity, and specificity (refer to Table 3). On the other hand, ResNet50
showed the worst performance out of all the measurements.

Table 3: Monkeypox prediction performance of all models on the training set
using various statistical measurements for Study One.

Model | Performance |
---|---|---
Accuracy | Precision | Recall | F1-Score | Sensitivity | Specificity
VGG16 | 0.98 | 0.98 | 0.98 | 0.98 | 1.0 | 0.96
InceptionResNetV2 | 1 | 1 | 1 | 1 | 1 | 1
ResNet50 | 0.56 | 0.32 | 0.57 | 0.41 | 1 | 0
ResNet101 | 0.68 | 0.80 | 0.68 | 0.63 | 1 | 0.26
MobileNetV2 | 1 | 1 | 1 | 1 | 1 | 1
VGG19 | 0.98 | 0.98 | 0.98 | 0.98 | 1 | 0.96

The results of the performance of all of the models with regard to the test
set are presented in Table 4. While the models ResNet50 and ResNet101 showed
the least performance, InceptionResNetV2 and VGG19 demonstrated the best
performance across all criteria.

Table 4: Monkeypox prediction performance of all models on the testing set
using various statistical measurements for Study One.

Model | Performance |
---|---|---
Accuracy | Precision | Recall | F1-Score | Sensitivity | Specificity
VGG16 | 0.81 | 0.86 | 0.81 | 0.80 | 1 | 0.42
InceptionResNetV2 | 0.90 | 0.88 | 0.88 | 0.77 | 1 | 0.95
ResNet50 | 0.56 | 0.32 | 0.56 | 0.40 | 1 | 0
ResNet101 | 0.56 | 0.32 | 0.56 | 0.40 | 1 | 0
MobileNetV2 | 0.81 | 0.82 | 0.81 | 0.81 | 0.77 | 0.85
VGG19 | 0.93 | 0.94 | 0.94 | 0.94 | 1 | 0.85

###  3.2 Confusion matrix

The total prediction of the model can be represented using confusion matrices.
Figure 5 illustrates the confusion matrices acquired for the train set during
Study One. Both the InceptionResNetV2 and MobileNetV2 models accurately
categorized all patients, as shown in the Figure 5. Contrary, VGG16, ResNet50,
ResNet101 and VGG19 misclassified 1, 26, 19 and 1 patients respectively. As a
result, we can draw the conclusion that the performance of the ResNet50 model
is the worst among all of the models based on the assessment of the confusion
matrices.

Figure 5: Study one confusion matrices for the train set.

Figure 6 displays the confusion matrices for Study One on the test set. Models
InceptionResNetV2 displayed the best performance by misclassifying only two
patients, while both ResNet50 and ResNet101 demonstrated the worst performance
by misclassifying seven patients. Considering the performance on both the
train and test set, it can be inferred that the performance of modified
InceptionResNetV2 remains constant for both train and test set, ultimately
demonstrated best performance compared to all other models.

Figure 6: Study one confusion matrices for the test set.

###  3.3 Models’ performance during training

Figure 7 shows train and test performance during each epoch for the CNN models
used in this study. The figure shows that the model’s performance during each
epoch improves for InceptionResNetV2 and VGG19 until 50 epochs. On the other
hand, ResNet50 and ResNet101’s performance fluctuated during each epoch and
overfitted at 50 epochs.

Figure 7: Modified models accuracy and loss during each epoch applied to Study
One. TA—train accuracy, VA—validation accuracy, TL—train loss, VS—validation
loss.

####  3.3.1 Explainable AI with Lime

Figure 8 explains the model’s prediction using LIME. The best performance was
observed when LIME model is used with 150 perturbation. The difference between
each perturbation was calculated by employing cosine metric with a kernel size
of 0.2. A simple linear interpolation is used to provide the explanation of
the models. Furthermore, the coefficient was calculated for each superpixel
value. Finally, top four features are identified that potentially affect the
model’s prediction.

Figure 8: The top four features that facilitated the classification and
misclassification of monkeypox patients from infected or non-infected images
during Study One.

###  3.4 Study Two

In Study Two, on the train set (refer to Table 5), InceptionResNetV2 and
MobileNetV2 outperformed all other models across all measures, while ResNet50
and ResNet101 demonstrated the worst performance.

Table 5: Monkeypox prediction performance of all models on training set using
various statistical measurements for Study Two.

Model | Performance |
---|---|---
Accuracy | Precision | Recall | F1-Score | Sensitivity | Specificity
VGG16 | 0.97 | 0.98 | 0.97 | 0.98 | 0.99 | 0.94
InceptionResNetV2 | 1 | 1 | 1 | 1 | 1 | 1
ResNet50 | 0.72 | 0.51 | 0.72 | 0.6 | 1 | 0
ResNet101 | 0.72 | 0.36 | 0.50 | 0.42 | 1 | 0
MobileNetV2 | 1 | 1 | 1 | 1 | 1 | 1
VGG19 | 0.94 | 0.94 | 0.92 | 0.93 | 0.97 | 0.85

Table 6 shows the test performance of various models applied during study two
as a transfer learning approach. The highest performance was observed for
MobileNetV2, and the lowest performance was identified for ResNet101 across
all measures.

Table 6: Monkeypox prediction performance of all models on training set using
various statistical measurements for Study Two.

Model | Performance |
---|---|---
Accuracy | Precision | Recall | F1-Score | Sensitivity | Specificity
VGG16 | 0.93 | 0.93 | 0.90 | 0.91 | 0.97 | 0.82
InceptionResNetV2 | 0.98 | 0.98 | 0.97 | 0.98 | 0.99 | 0.95
ResNet50 | 0.72 | 0.51 | 0.72 | 0.6 | 1 | 0
ResNet101 | 0.72 | 0.36 | 0.50 | 0.42 | 1 | 0
MobileNetV2 | 0.99 | 0.99 | 0.99 | 0.99 | 1 | 0.97
VGG19 | 0.90 | 0.89 | 0.86 | 0.87 | 0.95 | 0.76

####  3.4.1 Confusion matrices

Figure 9 shows the confusion matrices for the train set during Study Two. Both
the InceptionResNetV2 and MobileNetV2 accurately categorized all patients, as
seen in the Figure 9. In contrast, VGG16, ResNet50, Resnet101 and VGG19
misclassified 15, 185, 185 and 39 patients respectively. As a result, we are
able to draw the conclusion that the performance of the ResNet50 and ResNet101
models is the worst out of all of them based on the analysis of the confusion
matrices.

Figure 9: Study Two confusion matrices for the train set.

Figure 10 displays the confusion matrices for Study One on the test set.
MobileNetV2 displayed the best performance by misclassifying only one patient,
while ResNet50 and ResNet101 indicated the worst performance by misclassifying
46 patients. Considering both train and test analysis, it can be inferred that
the best performance of modified MobileNetV2 remains constant for both train
and test sets.

Figure 10: Study Two confusion matrices for the test set.

####  3.4.2 Models’ performance during training

Figure 11 plotted train and test results during each epoch for all the CNN
models for Study Two. The figure shows that the model’s performance during
each epoch shows better performance for most of the models except ResNet50 and
ResNet101. The accuracy and loss for both the train and test set fluctuated
and overfitted for models ResNet50 and ResNet101 after 25 epochs.

Figure 11: Modified models accuracy and loss during each epoch applied to
Study Two. TA—train accuracy, VA—validation accuracy, TL—train loss,
VS—validation loss

####  3.4.3 Explainable AI with Lime

The model’s performance and explanation found in Study one align with Study
Two. As shown in Figure 12, it is clear that ResNet50 and ResNet101
demonstrated the least performance by misclassifying Monkeypox patients.

Figure 12: The top four features that facilitated the classification and
misclassification of monkeypox patients from infected or non-infected hand
images during Study Two.

##  4 Discussion

This study proposes and evaluates six distinct modified deep learning-based
models in an effort to identify between patients with monkeypox and non-
monkeypox symptoms. Our dataset contains limited samples, and we have further
evaluated our model’s accuracy with 95% confidence intervals inspired by
referenced previous literature [34, 19, 21]. Table 7 shows model accuracy for
Study One with 95% confidence intervals for the train and test set. Two
approaches have been applied to measure the performance with CI: Wilson score
and binomial proportional interval. As stated in Table 7, InceptionResNetV2
exceeded all other models in terms of accuracy. In contrast, ResNet50
demonstrates significantly lower performance on the train and test set while
measuring the accuracy with CI.

Table 7: Confidence Interval ($\alpha=0.05$) of all models used in Study One
in terms of accuracy.

Study One | Model | Train Accuracy | Methods |
---|---|---|---|---
| Wilson Score | Binomial proportion interval
Train set | VGG16 | 0.98 | 0.851-0.994 | 0.914-1.0
InceptionResNetV2 | 1 | 0.93-0.99 | 1.00-1.00
ResNet50 | 0.56 | 0.44-0.68 | 0.44-0.692
ResNet101 | 0.68 | 0.55-0.78 | 0.56-0.80
MobileNetV2 | 1 | 0.93-0.99 | 1.0-1.0
VGG19 | 0.98 | 0.91-0.99 | 0.95-1.0
Test set | VGG16 | 0.81 | 0.56-0.93 | 0.62-1.0
InceptionResNetV2 | 0.87 | 0.63-0.96 | 0.713-1.0
ResNet50 | 0.56 | 0.33-0.76 | 0.32-0.81
ResNet101 | 0.56 | 0.33-0.76 | 0.32-0.81
MobileNetV2 | 0.81 | 0.56-0.93 | 0.62-1.0
VGG19 | 0.93 | 0.71-0.98 | 0.81-1.0

Table 8: Confidence Interval ($\alpha=0.05$) of all models used in Study Two
in terms of accuracy.

Study Two | Model | Train Accuracy | Methods |
---|---|---|---|---
| Wilson Score | Binomial proportion interval
Train set | VGG16 | 0.97 | 0.95-0.98 | 0.95-0.98
InceptionResNetV2 | 1 | 0.99-1.0 | 1.0-1.0
ResNet50 | 0.72 | 0.68-0.75 | 0.68-0.75
ResNet101 | 0.72 | 0.68-0.75 | 0.68-0.75
MobileNetV2 | 1 | 0.99-1.0 | 1.0-1.0
VGG19 | 0.94 | 0.91-0.95 | 0.92-0.95
Test set | VGG16 | 0.93 | 0.88-0.96 | 0.89-0.97
InceptionResNetV2 | 0.98 | 0.94-0.99 | 0.961-1.0
ResNet50 | 0.72 | 0.68-0.75 | 0.68-0.75
ResNet101 | 0.72 | 0.68-0.75 | 0.68-0.75
MobileNetV2 | 0.99 | 0.96-0.99 | 0.98-1.0
VGG19 | 0.90 | 0.84-0.93 | 0.85-0.94

Table 8 manifests the accuracy performance of six different models for Study
Two. As stated in the table, MobileNetV2 outperformed all other models by
showing better performance while ResNet50 and ResNet101 demonstrate the worst
performance. Further, we have used Heatmap as a data visualization technique
that demonstrates the phenomenon as color in two dimensions for different CNN
layers during the prediction. Using heatmap, it is easy to identify the
specific region where CNN establishes more focus during the prediction. The
idea is to use the weights from the last dense layers and multiply them with
the final CNN layer. In this work, we identify the potential Monkeypox
infected areas using global average pooling (GAP). Class activation map (CAM)
is initially used to collect each output of the convolutional layer and
combine it in one shot. Figure 13(a) represents the spotted region by the
ResNet50 models, whereas Figure 13(b) shows the heatmap on different spotted
regions and Figure 13(c) on actual images. From the overall analysis across
various measures, it can be inferred that MobileNetV2 and InceptionResNetV2
are the best models that can detect Monkeypox patients with perfect accuracy.
It is relevant to emphasize that, at the time of experiment, there was no
published or preprint literature where the transfer learning approach or deep
learning approach was utilized to develop an AI-driven model to detect
Monkeypox patients. Consequently, we could not evaluate our model’s
performance by comparing it with others’ work. However, our higher performance
results align with many referenced research, which established the
potentiality of the transfer learning approach to develop image-based disease
diagnosis. Examples include the InceptionResNetV2 model proposed by Narin et
al. (2020), which trained with only 100 images to detect COVID-19 patients.
Fujisawa et al. (2022) developed a deep learning-based model to detect skin
tumors. The model was developed with 1842 patient data and achieved an
accuracy of 76.5%. Our proposed model predictions are explained with LIME and
further cross-checked by expert doctors’ opinions. Our findings suggest that
our modified MobileNetV2 and InceptionResNetV2 demonstrated a significant
contribution to identifying the top feature that characterizes the onset of
Monkeypox by identifying essential features from the images.

Figure 13: Heatmap of class activation on Monkeypox patient image.

To further aid in comparing the model’s final prediction with the visual
evidence, we have also employed an image segmentation method to highlight
highly infected areas. Figure 14 depicts the outcomes of threshold-based
segmentation applied to images of hands infected with the Monkeypox virus. The
region of interest (ROI) has been defined using the pixel values of the
infected areas. Then, a mask is generated for use in segmenting and generating
the final output.

Figure 14: Threshold-based segmentation (a) Original, (b) ROI, (c) Mask, (d)
Segmented

The major purpose of this research was to create a deep learning-based model
capable of detecting patients with Monkeypox symptoms using a dataset of
images of Monkeypox patients. Due to a lack of data, the scope of the current
literature in this topic remains limited at this time. In the near future, it
will be fascinating to examine how our suggested model performs on the
multiclass and a large dataset.

##  5 Conclusion

This work proposed and tested six alternative modified deep learning models
for detecting Monkeypox disease. We discovered that a modified MobileNetV2 and
InceptionResNetV2 could identify between Monkeypox and Non-Monkeypox patients
with an accuracy ranging from 90% to 100%. In addition to that, we have
applied LIME, in order to comprehend and verify the predictions made by our
model, demonstrating our suggested models’ superiority in detecting Monkeypox
illnesses. We expect that our study outcomes will provide future academics and
practitioners some insight into the use of transfer learning models and
explainable AI in constructing secure and trustworthy Monkeypox disease
diagnosis models. The steps that are being used during this study will be
considered in developing deep learning models using mixed data, evaluating the
model’s performance on the highly imbalanced dataset, creating fake samples
using Generative adversarial networks (GAN), and developing a mobile-based
user-friendly application that can be used to diagnose Monkeypox disease.

## References

  * [1] Livia V Patrono, Kamilla Pléh, Liran Samuni, Markus Ulrich, Caroline Röthemeier, Andreas Sachse, Silvia Muschter, Andreas Nitsche, Emmanuel Couacy-Hymann, Christophe Boesch, et al.  Monkeypox virus emergence in wild chimpanzees reveals distinct clinical outcomes and viral diversity.  Nature Microbiology, 5(7):955–965, 2020.
  * [2] Scott Parker, Anthony Nuara, R Mark L Buller, and Denise A Schultz.  Human monkeypox: an emerging zoonotic disease.  Future Microbiology, 2007.
  * [3] Samson Wong, Susanna Lau, Patrick Woo, and Kwok-Yung Yuen.  Bats as a continuing source of emerging infections in humans.  Reviews in medical virology, 17(2):67–91, 2007.
  * [4] Centers for Disease Control and Prevention.  Monkeypox Signs and Symptoms., 2022.  Last accessed May 30, 2022.
  * [5] Mary G Reynolds, Andrea M McCollum, Beatrice Nguete, Robert Shongo Lushima, and Brett W Petersen.  Improving the care and treatment of monkeypox patients in low-resource settings: applying evidence from contemporary biomedical and smallpox biodefense research.  Viruses, 9(12):380, 2017.
  * [6] Anthony S Fauci.  Emerging and reemerging infectious diseases: the perpetual challenge.  Academic Medicine, 80(12):1079–1085, 2005.
  * [7] Michaeleen Doucleff.  Scientists warned us about monkeypox in 1988. Here’s why they were right., 2022.  Last accessed May 27, 2022.
  * [8] Centers for Disease Control and Prevention.  2022 Monkeypox and Orthopoxvirus Outbreak Global Map., 2022.  Last accessed June 07, 2022.
  * [9] Md Manjurul Ahsan, Muhammad Ramiz Uddin, Mithila Farjana, Ahmed Nazmus Sakib, Khondhaker Al Momin, and Shahana Akter Luna.  Image data collection and implementation of deep learning-based model in detecting monkeypox disease using modified vgg16.  arXiv preprint arXiv:2206.01862, 2022.
  * [10] Md Manjurul Ahsan, Muhammad Ramiz Uddin, and Shahana Akter Luna.  Monkeypox image data collection.  arXiv preprint arXiv:2206.01774, 2022.
  * [11] Centers for Disease Control and Prevention.  Monkeypox and Smallpox Vaccine., 2022.  Last accessed May 30, 2022.
  * [12] David L Heymann, Mark Szczeniowski, and Karin Esteves.  Re-emergence of monkeypox in africa: a review of the past six years.  British medical bulletin, 54(3):693–702, 1998.
  * [13] Centers for Disease Control and Prevention.  Diagnostic Tests., 2022.  Last accessed May 30, 2022.
  * [14] Md Manjurul Ahsan, Md Tanvir Ahad, Farzana Akter Soma, Shuva Paul, Ananna Chowdhury, Shahana Akter Luna, Munshi Md Shafwat Yazdan, Akhlaqur Rahman, Zahed Siddique, and Pedro Huebner.  Detecting sars-cov-2 from chest x-ray using artificial intelligence.  Ieee Access, 9:35501–35513, 2021.
  * [15] Md Manjurul Ahsan, Tasfiq E. Alam, Theodore Trafalis, and Pedro Huebner.  Deep mlp-cnn model using mixed-data to distinguish between covid-19 and non-covid-19 patients.  Symmetry, 12(9):1526, 2020.
  * [16] Md Manjurul Ahsan and Zahed Siddique.  Machine learning-based heart disease diagnosis: A systematic literature review.  Artificial Intelligence in Medicine, page 102289, 2022.
  * [17] Md Manjurul Ahsan, Shahana Akter Luna, and Zahed Siddique.  Machine-learning-based disease diagnosis: A comprehensive review.  In Healthcare, volume 10, page 541. MDPI, 2022.
  * [18] Sangwon Chae, Sungjun Kwon, and Donghyun Lee.  Predicting infectious disease using deep learning and big data.  International journal of environmental research and public health, 15(8):1596, 2018.
  * [19] R Arias and J Mejía.  Varicella zoster early detection with deep learning.  In 2020 IEEE Engineering International Research Conference (EIRCON), pages 1–4. IEEE, 2020.
  * [20] Shuchi Bhadula, Sachin Sharma, Piyush Juyal, and Chitransh Kulshrestha.  Machine learning algorithms based skin disease detection.  International Journal of Innovative Technology and Exploring Engineering (IJITEE), 9(2), 2019.
  * [21] Kittipat Sriwong, Supaporn Bunrit, Kittisak Kerdprasop, and Nittaya Kerdprasop.  Dermatological classification using deep learning of skin image and patient background knowledge.  International Journal of Machine Learning and Computing, 9(6):862–867, 2019.
  * [22] Karen Simonyan and Andrew Zisserman.  Very deep convolutional networks for large-scale image recognition.  arXiv preprint arXiv:1409.1556, 2014.
  * [23] Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi.  Inception-v4, inception-resnet and the impact of residual connections on learning.  In Thirty-first AAAI conference on artificial intelligence, page 4278–4284, 2017.
  * [24] Takuya Akiba, Shuji Suzuki, and Keisuke Fukuda.  Extremely large minibatch sgd: Training resnet-50 on imagenet in 15 minutes.  arXiv preprint arXiv:1711.04325, 2017.
  * [25] Warid Islam, Meredith Jones, Rowzat Faiz, Negar Sadeghipour, Yuchen Qiu, and Bin Zheng.  Improving performance of breast lesion classification using a resnet50 model optimized with a novel attention mechanism.  Tomography, 8(5):2411–2425, 2022.
  * [26] Warid Islam, Gopichandh Danala, Huong Pham, and Bin Zheng.  Improving the performance of computer-aided classification of breast lesions using a new feature fusion method.  In Medical Imaging 2022: Computer-Aided Diagnosis, volume 12033, pages 84–91. SPIE, 2022.
  * [27] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun.  Deep residual learning for image recognition.  In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
  * [28] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen.  Mobilenetv2: Inverted residuals and linear bottlenecks.  In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4510–4520, 2018.
  * [29] Meredith A Jones, Warid Islam, Rozwat Faiz, Xuxin Chen, and Bin Zheng.  Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction.  Frontiers in Oncology, 12, 2022.
  * [30] Md Shahin Ali, Md Sipon Miah, Jahurul Haque, Md Mahbubur Rahman, and Md Khairul Islam.  An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models.  Machine Learning with Applications, 5:100036, 2021.
  * [31] Arkadiusz Kwasigroch, Agnieszka Mikołajczyk, and Michał Grochowski.  Deep neural networks approach to skin lesions classification—a comparative analysis.  In 2017 22nd international conference on methods and models in automation and robotics (MMAR), pages 1069–1074. IEEE, 2017.
  * [32] Md Manjurul Ahsan, Yueqing Li, Jing Zhang, Md Tanvir Ahad, Munshi Md Yazdan, et al.  Face recognition in an unconstrained and real-time environment using novel bmc-lbph methods incorporates with dji vision sensor.  Journal of Sensor and Actuator Networks, 9(4):54, 2020.
  * [33] Md Manjurul Ahsan, Yueqing Li, Jing Zhang, Md Tanvir Ahad, and Kishor Datta Gupta.  Evaluating the performance of eigenface, fisherface, and local binary pattern histogram-based facial recognition methods under various weather conditions.  Technologies, 9(2):31, 2021.
  * [34] Ali Narin, Ceren Kaya, and Ziynet Pamuk.  Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks.  Pattern Analysis and Applications, 24(3):1207–1220, 2021.
  * [35] Alaa Ali Hameed, Bekir Karlik, and Mohammad Shukri Salman.  Back-propagation algorithm with variable adaptive momentum.  Knowledge-Based Systems, 114:79–87, 2016.


* Kounkel et al. (2017) Kounkel, M., Hartmann, L., Loinard, L., Ortiz-León, G. N., Mioduszewski, A. J., Rodríguez, L. F., et al. (2017).  The gould’s belt distances survey (gobelins). ii. distances and structure toward the orion molecular clouds.  _The Astrophysical Journal_ 834, 142
  * Kwon et al. (2022) Kwon, W., Pattle, K., Sadavoy, S., Hull, C. L. H., Johnstone, D., Ward-Thompson, D., et al. (2022).  B-fields in Star-forming Region Observations (BISTRO): Magnetic Fields in the Filamentary Structures of Serpens Main.  _ApJ_ 926, 163.  10.3847/1538-4357/ac4bbe
  * Lazarian (2007) Lazarian, A. (2007).  Tracing magnetic fields with aligned grains.  _JQSRT_ 106, 225–256.  10.1016/j.jqsrt.2007.01.038
  * Lazarian et al. (2015) Lazarian, A., Andersson, B. G., and Hoang, T. (2015).  Grain alignment: Role of radiative torques and paramagnetic relaxation.  In _Polarimetry of Stars and Planetary Systems_. 81
  * Lazarian and Draine (1999) Lazarian, A. and Draine, B. T. (1999).  Nuclear Spin Relaxation within Interstellar Grains.  _ApJL_ 520, L67–L70.  10.1086/312137
  * Lazarian and Efroimsky (1999) Lazarian, A. and Efroimsky, M. (1999).  Inelastic dissipation in a freely rotating body: application to cosmic dust alignment.  _MNRAS_ 303, 673–684.  10.1046/j.1365-8711.1999.02235.x
  * Lazarian and Hoang (2007a) Lazarian, A. and Hoang, T. (2007a).  Radiative torques: analytical model and basic properties.  _MNRAS_ 378, 910–946.  10.1111/j.1365-2966.2007.11817.x
  * Lazarian and Hoang (2007b) Lazarian, A. and Hoang, T. (2007b).  Subsonic Mechanical Alignment of Irregular Grains.  _ApJL_ 669, L77–L80.  10.1086/523849
  * Lazarian and Hoang (2008) Lazarian, A. and Hoang, T. (2008).  Alignment of Dust with Magnetic Inclusions: Radiative Torques and Superparamagnetic Barnett and Nuclear Relaxation.  _ApJL_ 676, L25.  10.1086/586706
  * Lazarian and Hoang (2021) Lazarian, A. and Hoang, T. (2021).  Alignment and Rotational Disruption of Dust.  _ApJ_ 908, 12.  10.3847/1538-4357/abd02c
  * Lee et al. (2020) Lee, H., Hoang, T., Le, N., and Cho, J. (2020).  Physical Model of Dust Polarization by Radiative Torque Alignment and Disruption and Implications for Grain Internal Structures.  _ApJ_ 896, 44.  10.3847/1538-4357/ab8e33
  * Li et al. (2008) Li, H., Dowell, C. D., Kirby, L., Novak, G., and Vaillancourt, J. E. (2008).  Design and initial performance of SHARP, a polarimeter for the SHARC-II camera at the Caltech Submillimeter Observatory.  _ApOpt_ 47, 422–430.  10.1364/AO.47.000422
  * Lim et al. (2020) Lim, W., Buizer, J. M. D., and Radomski, J. T. (2020).  Surveying the giant h ii regions of the milky way with SOFIA. II. m17.  _The Astrophysical Journal_ 888, 98.  10.3847/1538-4357/ab5fd0
  * Lopez et al. (2011) Lopez, L. A., Krumholz, M. R., Bolatto, A. D., Prochaska, J. X., and Ramirez-Ruiz, E. (2011).  What Drives the Expansion of Giant H II Regions?: A Study of Stellar Feedback in 30 Doradus.  _ApJ_ 731, 91.  10.1088/0004-637X/731/2/91
  * Lopez-Rodriguez (2021) Lopez-Rodriguez, E. (2021).  The magnetic field across the molecular warped disk of Centaurus A.  _Nature Astronomy_ 5, 604–614.  10.1038/s41550-021-01329-9
  * Lopez-Rodriguez et al. (2020) Lopez-Rodriguez, E., Dowell, C. D., Jones, T. J., Harper, D. A., Berthoud, M., Chuss, D., et al. (2020).  SOFIA/HAWC+ Traces the Magnetic Fields in NGC 1068.  _ApJ_ 888, 66.  10.3847/1538-4357/ab5849
  * Marrone and Rao (2008) Marrone, D. P. and Rao, R. (2008).  The submillimeter array polarimeter.  In _Millimeter and Submillimeter Detectors and Instrumentation for Astronomy IV_ , eds. W. D. Duncan, W. S. Holland, S. Withington, and J. Zmuidzinas. vol. 7020 of _Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series_ , 70202B.  10.1117/12.788677
  * Mathis et al. (1983) Mathis, J. S., Mezger, P. G., and Panagia, N. (1983).  Interstellar radiation field and dust temperatures in the diffuse interstellar matter and in giant molecular clouds.  _A &A_ 500, 259–276
  * Menten et al. (2007) Menten, K. M., Reid, M. J., Forbrich, J., and Brunthaler, A. (2007).  The distance to the Orion Nebula.  _A &A_ 474, 515–520.  10.1051/0004-6361:20078247
  * Misawa et al. (2014) Misawa, R., Bernard, J. P., Ade, P., André, Y., de Bernardis, P., Bouzit, M., et al. (2014).  PILOT: a balloon-borne experiment to measure the polarized FIR emission of dust grains in the interstellar medium.  In _Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy VII_ , eds. W. S. Holland and J. Zmuidzinas. vol. 9153 of _Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series_ , 91531H.  10.1117/12.2055506
  * Nagai et al. (2016) Nagai, H., Nakanishi, K., Paladino, R., Hull, C. L. H., Cortes, P., Moellenbrock, G., et al. (2016).  ALMA Science Verification Data: Millimeter Continuum Polarimetry of the Bright Radio Quasar 3C 286.  _ApJ_ 824, 132.  10.3847/0004-637X/824/2/132
  * Ngoc et al. (2021) Ngoc, N. B., Diep, P. N., Parsons, H., Pattle, K., Hoang, T., Ward-Thompson, D., et al. (2021).  Observations of Magnetic Fields Surrounding LkH$\alpha$ 101 Taken by the BISTRO Survey with JCMT-POL-2.  _ApJ_ 908, 10.  10.3847/1538-4357/abd0fc
  * Ortiz-León et al. (2017) Ortiz-León, G. N., Loinard, L., Kounkel, M. A., Dzib, S. A., Mioduszewski, A. J., Rodríguez, L. F., et al. (2017).  The Gould’s Belt Distances Survey (GOBELINS). I. Trigonometric Parallax Distances and Depth of the Ophiuchus Complex.  _ApJ_ 834, 141.  10.3847/1538-4357/834/2/141
  * Pattle and Fissel (2019) Pattle, K. and Fissel, L. (2019).  Submillimeter and Far-infrared Polarimetric Observations of Magnetic Fields in Star-Forming Regions.  _Frontiers in Astronomy and Space Sciences_ 6, 15.  10.3389/fspas.2019.00015
  * Pattle et al. (2022) Pattle, K., Fissel, L., Tahani, M., Liu, T., and Ntormousi, E. (2022).  Magnetic fields in star formation: from clouds to cores.  _arXiv e-prints_ , arXiv:2203.11179
  * Pattle et al. (2021a) Pattle, K., Gear, W., Redman, M., Smith, M. W. L., and Greaves, J. (2021a).  Submillimetre observations of the two-component magnetic field in M82.  _MNRAS_ 505, 684–688.  10.1093/mnras/stab1300
  * Pattle et al. (2019) Pattle, K., Lai, S.-P., Hasegawa, T., Wang, J.-W., Furuya, R. S., Ward-Thompson, D., et al. (2019).  JCMT BISTRO Survey Observations of the Ophiuchus Molecular Cloud: Dust Grain Alignment Properties Inferred Using a Ricean Noise Model.  _ApJ_ 880, 27.  10.3847/1538-4357/ab286f
  * Pattle et al. (2021b) Pattle, K., Lai, S.-P., Wright, M., Coudé, S., Plambeck, R., Hoang, T., et al. (2021b).  OMC-1 dust polarization in ALMA Band 7: diagnosing grain alignment mechanisms in the vicinity of Orion Source I.  _MNRAS_ 503, 3414–3433.  10.1093/mnras/stab608
  * Pattle et al. (2017) Pattle, K., Ward-Thompson, D., Berry, D., Hatchell, J., Chen, H.-R., Pon, A., et al. (2017).  The JCMT BISTRO Survey: The Magnetic Field Strength in the Orion A Filament.  _ApJ_ 846, 122.  10.3847/1538-4357/aa80e5
  * Pillai et al. (2020) Pillai, T. G. S., Clemens, D. P., Reissl, S., Myers, P. C., Kauffmann, J., Lopez-Rodriguez, E., et al. (2020).  Magnetized filamentary gas flows feeding the young embedded cluster in Serpens South.  _Nature Astronomy_ 4, 1195–1201.  10.1038/s41550-020-1172-6
  * Planck Collaboration et al. (2014) Planck Collaboration, Ade, P. A. R., Aghanim, N., Alves, M. I. R., Armitage-Caplan, C., Arnaud, M., et al. (2014).  Planck 2013 results. I. Overview of products and scientific results.  _A &A_ 571, A1.  10.1051/0004-6361/201321529
  * Planck Collaboration: et al. (2015) Planck Collaboration:, Ade, P. A. R., Aghanim, N., Alina, D., Alves, M. I. R., Armitage-Caplan, C., et al. (2015).  Planck intermediate results. xix. an overview of the polarized thermal emission from galactic dust.  _A &A_ 576, A104.  10.1051/0004-6361/201424082
  * Planck Collaboration et al. (2020) Planck Collaboration, Aghanim, N., Akrami, Y., Alves, M. I. R., Ashdown, M., Aumont, J., et al. (2020).  Planck 2018 results. XII. Galactic astrophysics using polarized dust emission.  _A &A_ 641, A12.  10.1051/0004-6361/201833885
  * Povich et al. (2009) Povich, M. S., Churchwell, E., Bieging, J. H., Kang, M., Whitney, B. A., Brogan, C. L., et al. (2009).  THE EXTENDED ENVIRONMENT OF m17: A STAR FORMATION HISTORY.  _The Astrophysical Journal_ 696, 1278–1306.  10.1088/0004-637x/696/2/1278
  * Purcell (1979) Purcell, E. M. (1979).  Suprathermal rotation of interstellar grains.  _ApJ_ 231, 404–416.  10.1086/157204
  * Reissl et al. (2016) Reissl, S., Wolf, S., and Brauer, R. (2016).  Radiative transfer with POLARIS. I. Analysis of magnetic fields through synthetic dust continuum polarization measurements.  _A &A_ 593, A87.  10.1051/0004-6361/201424930
  * Roman-Duval et al. (2014) Roman-Duval, J., Gordon, K. D., Meixner, M., Bot, C., Bolatto, A., Hughes, A., et al. (2014).  Dust and Gas in the Magellanic Clouds from the HERITAGE Herschel Key Project. II. Gas-to-dust Ratio Variations across Interstellar Medium Phases.  _ApJ_ 797, 86.  10.1088/0004-637X/797/2/86
  * Sadavoy et al. (2019) Sadavoy, S. I., Stephens, I. W., Myers, P. C., Looney, L., Tobin, J., Kwon, W., et al. (2019).  Dust Polarization toward Embedded Protostars in Ophiuchus with ALMA. III. Survey Overview.  _The Astrophysical Journal Supplement Series_ 245, 2.  10.3847/1538-4365/ab4257
  * Santos et al. (2019) Santos, F. P., Chuss, D. T., Dowell, C. D., Houde, M., Looney, L. W., Lopez Rodriguez, E., et al. (2019).  The Far-infrared Polarization Spectrum of $\rho$ Ophiuchi A from HAWC+/SOFIA Observations.  _ApJ_ 882, 113.  10.3847/1538-4357/ab3407
  * Tazaki et al. (2017) Tazaki, R., Lazarian, A., and Nomura, H. (2017).  Radiative Grain Alignment In Protoplanetary Disks: Implications for Polarimetric Observations.  _ApJ_ 839, 56.  10.3847/1538-4357/839/1/56
  * Tercero et al. (2018) Tercero, B., Cuadrado, S., López, A., Brouillet, N., Despois, D., and Cernicharo, J. (2018).  Chemical segregation of complex organic O-bearing species in Orion KL.  _A &A_ 620, L6.  10.1051/0004-6361/201834417
  * Tram et al. (2022) Tram, L. N., Bonne, L., Hu, Y., Lopez-Rodriguez, E., Guerra, J. A., Lesaffre, P., et al. (2022).  SOFIA observations of 30 Doradus: II – Magnetic fields and large scale gas kinematics.  _arXiv e-prints_ , arXiv:2205.12084
  * Tram et al. (2021a) Tram, L. N., Hoang, T., Lee, H., Santos, F. P., Soam, A., Lesaffre, P., et al. (2021a).  Understanding Polarized Dust Emission from $\rho$ Ophiuchi A in Light of Grain Alignment and Disruption by Radiative Torques.  _ApJ_ 906, 115.  10.3847/1538-4357/abc6fe
  * Tram et al. (2021b) Tram, L. N., Hoang, T., Lopez-Rodriguez, E., Coudé, S., Soam, A., Andersson, B. G., et al. (2021b).  SOFIA Observations of 30 Doradus. I. Far-infrared Dust Polarization and Implications for Grain Alignment and Disruption by Radiative Torques.  _ApJ_ 923, 130.  10.3847/1538-4357/ac13a1
  * Tram et al. (2020) Tram, L. N., Hoang, T., Soam, A., Lesaffre, P., and Reach, W. T. (2020).  Modeling Rotational Disruption of Grains and Microwave Emission from Spinning Dust in AGB Envelopes.  _ApJ_ 893, 138.  10.3847/1538-4357/ab7b5e
  * Tram et al. (2021c) Tram, L. N., Lee, H., Hoang, T., Michail, J. M., Chuss, D. T., Nickerson, S., et al. (2021c).  Observational Evidence for Rotational Desorption of Complex Molecules by Radiative Torques from Orion BN/KL.  _ApJ_ 908, 159.  10.3847/1538-4357/abccbe
  * Tung and Hoang (2020) Tung, N.-D. and Hoang, T. (2020).  Rotational Disruption of Dust and Ice by Radiative Torques in Protoplanetary Disks and the Implications for Observations.  _ApJ_ 901, 6.  10.3847/1538-4357/abacbf
  * van Dishoeck (2014) van Dishoeck, E. F. (2014).  Astrochemistry of dust, ice and gas: introduction and overview.  _Faraday Discussions_ 168, 9.  10.1039/C4FD00140K
  * Vrba (1977) Vrba, F. J. (1977).  Role of magnetic fields in the evolution of five dark cloud complexes.  _AJ_ 82, 198–208.  10.1086/112031
  * Whittet et al. (2008) Whittet, D. C. B., Hough, J. H., Lazarian, A., and Hoang, T. (2008).  The Efficiency of Grain Alignment in Dense Interstellar Clouds: a Reassessment of Constraints from Near-Infrared Polarization.  _ApJ_ 674, 304–315.  10.1086/525040
  * Wiesemeyer et al. (2014) Wiesemeyer, H., Hezareh, T., Kreysa, E., Weiss, A., Güsten, R., Menten, K. M., et al. (2014).  Submillimeter Polarimetry with PolKa, a Reflection-Type Modulator for the APEX Telescope.  _PASP_ 126, 1027.  10.1086/679002
  * Xu et al. (2011) Xu, Y., Moscadelli, L., Reid, M. J., Menten, K. M., Zhang, B., Zheng, X. W., et al. (2011).  TRIGONOMETRIC PARALLAXES OF MASSIVE STAR-FORMING REGIONS. VIII. g12.89+0.49, g15.03–0.68 (m17), AND g27.36–0.16.  _The Astrophysical Journal_ 733, 25.  10.1088/0004-637x/733/1/25
  * Zhang et al. (2014) Zhang, Q., Qiu, K., Girart, J. M., Liu, H. B., Tang, Y.-W., Koch, P. M., et al. (2014).  Magnetic Fields and Massive Star Formation.  _ApJ_ 792, 116.  10.1088/0004-637X/792/2/116
  * Zielinski et al. (2021) Zielinski, N., Wolf, S., and Brunngräber, R. (2021).  Constraining the magnetic field properties of Bok globule B335 using SOFIA/HAWC+.  _A &A_ 645, A125.  10.1051/0004-6361/202039126