# Adaptive Local Binary Pattern: A Novel Feature Descriptor for Enhanced
Analysis of Kidney Abnormalities in CT Scan Images using ensemble based
Machine Learning Approach

Tahmim Hossain
University of Alberta
<EMAIL_ADDRESS>Faisal Sayed
University of Alberta
<EMAIL_ADDRESS>Solehin Islam
University of Alberta
<EMAIL_ADDRESS>
## Abstract

The shortage of nephrologists and the growing public health concern over renal
failure have spurred the demand for AI systems capable of autonomously
detecting kidney abnormalities. Renal failure, marked by a gradual decline in
kidney function, can result from factors like cysts, stones, and tumors.
Chronic kidney disease may go unnoticed initially, leading to untreated cases
until they reach an advanced stage. The dataset, comprising 12,427 images from
multiple hospitals in Dhaka, was categorized into four groups: cyst, tumor,
stone, and normal. Our methodology aims to enhance CT scan image quality using
Cropping, Resizing, and CALHE techniques, followed by feature extraction with
our proposed Adaptive Local Binary Pattern (A-LBP) feature extraction method
compared with the state-of-the-art local binary pattern (LBP) method. Our
proposed features fed into classifiers such as Random Forest, Decision Tree,
Naive Bayes, K-Nearest Neighbor, and SVM. We explored an ensemble model with
soft voting to get a more robust model for our task. We got the highest of
more than 99% in accuracy using our feature descriptor and ensembling five
classifiers (Random Forest, Decision Tree, Naive Bayes, K-Nearest Neighbor,
Support Vector Machine) with the soft voting method.

Index Terms - Feature Extraction, Kidney Disease, Classification, Adaptive
Local Binary Pattern

##  1 Introduction

The global health community acknowledges CKD as a significant public health
issue, emphasizing the importance of early detection, treatment, and its
relationship with other chronic diseases for improving outcomes and
implementing effective public health policies [27]. Chronic kidney disease
poses a global health threat, with prevalence estimated at 8–16%, driven
primarily by diabetes mellitus, and associated with various complications
including mortality, kidney disease progression, and cognitive decline, with
the poorest populations facing the highest risk [24]. The forecasted rise of
COPD, diabetes, chronic kidney disease, Alzheimer’s disease, and lung cancer
by 2040 suggests significant health system implications, with COPD projected
to increase most notably from 9th to 4th place [14]. Kidney cancer ranks sixth
among men in the US, with stones, cysts, and renal cell carcinoma being common
kidney disorders affecting renal function. Approximately 6% of women and 11%
of men in the US have experienced kidney stones [9]. Efficient identification
of kidney-related issues is crucial due to their widespread occurrence and
potential complications. Kidney stone disease affects 12% of the global
population, while renal cell carcinoma (RCC) is a prevalent malignancy. Kidney
cysts, including simple and complex varieties, can impact renal and blood
function, highlighting the need for accurate diagnosis to differentiate benign
from malignant cases [30]. Timely identification of kidney abnormalities like
cysts, stones, and tumors is essential for preventing renal failure,
emphasizing the significance of early diagnosis in renal health management
[18]. Rapid kidney-related radiological finding detection using artificial
intelligence (AI) models has great potential to support medical staff and ease
patient anxiety, solve the global radiology and nephrology shortage, and
capitalize on advances in deep learning for visual tasks [30].

The CT machine utilizes X-ray beams to scan specific areas of the body,
generating cross-sectional images that offer detailed three-dimensional
insights into the targeted anatomy, often employed alongside pathology tests
for diagnosing kidney diseases [35]. Because CT scans may provide detailed
slice-by-slice images as well as extensive three-dimensional data, they are
especially useful when evaluating kidney function that’s why it is better to
use the CT scan image. Renal failure can be avoided by treating kidney
abnormalities such as cysts, stones, and tumors as soon as possible [18].

In this study, we introduce a novel feature descriptor tailored to improve the
classification of medical CT scan images depicting kidney abnormalities. Our
proposed descriptor, Adaptive Local Binary Pattern (ABPL), offers significant
enhancements in discerning various kidney textures associated with normal,
cystic, stony, and tumorous conditions. While conventional methods often rely
on pre-trained models, CNNs, or established feature descriptors, our approach
breaks new ground by developing a bespoke descriptor inspired by the Local
Binary Pattern (LBP) technique. Through a thorough review of existing
literature, we observed a prevalent trend towards utilizing pre-existing
models or descriptors. However, our decision to innovate and craft a unique
descriptor results in notable improvements across all evaluation metrics,
marking a novel approach in this domain.

Our methodology involved several steps: firstly, we cropped the images to
remove unwanted portions, then resized them to fit the model, and normalized
the images. Subsequently, we employed the Contrast-Limited Adaptive Histogram
Equalization (CLAHE) method to enhance image contrast while preserving
details, particularly in areas with varying contrast levels, thus mitigating
the risk of noise over-amplification in low-contrast regions. IBy reviewing
different literature, we found several papers that utilized CLAHE as an image
enhancement technique for their datasets. Building upon this, we implemented
CLAHE as a preprocessing step in our methodology. Following preprocessing, we
applied feature descriptors to extract relevant features from the images.
Specifically, we utilized the Local Binary Pattern (LBP) as one feature
descriptor and introduced a novel feature descriptor called Adaptive Local
Binary Pattern (ALBP). These descriptors were crucial in capturing meaningful
information from the images. Subsequently, we fed the extracted features into
various classifiers, including Random Forest, Decision Tree, Naive Bayes,
K-Nearest Neighbor, SVM, and a soft voting ensemble technique, for the
classification of kidney abnormalities. Our experimental results revealed
significant improvements in evaluation metrics, demonstrating the
effectiveness of our proposed feature descriptor, as discussed in the
experimental results section of this paper.

##  2 Related Work

There are some recent research works where they tried to detect kidney
abnormalities from the CT scan or MRI images by using different types of
feature extraction techniques. Upon reviewing the extensive body of literature
encompassed by papers authored by George et al. [15], Georgieva et al. [16],
Mamatov et al. [29], Xie et al. [43], Vidyasaraswathi et al. [42], and Abdoun
[1], it becomes evident that the Contrast Limited Adaptive Histogram
Equalization (CLAHE) method serves as a cornerstone in the enhancement of CT
scan-based images. Its efficacy lies in its ability to accentuate details,
mitigate noise, and enhance contrast, thereby facilitating improved
visualization and interpretation of CT scans, which is crucial for accurate
diagnosis and treatment planning in kidney-related pathologies.

Hafizah et al. [20] opted for GLCM features, while Islam and Nahiduzzaman [23]
leveraged CNNs for feature extraction, reflecting the growing utilization of
deep learning in this domain. The utilization of Darknet-19 features by Baygin
et al. [7] underscores the diversity of approaches, as does the combination of
CRF with HOG by Bhole et al. [8]. Meanwhile, Vasanthselvakumar et al. [39]
focused exclusively on HOG, and Christiyana and Rajamani [12] proposed a
fusion of Laws Masks and Local Binary Patterns, showcasing the varied
strategies employed to extract meaningful features from kidney images.
Furthermore, Ali [3] and Kang et al. [25] explored Local Binary Patterns and
3D CNN with ConvLSTM, respectively, highlighting the versatility in feature
extraction methodologies adopted across the literature.

In classification techniques, authors have made strategic choices tailored to
the complexities of kidney image analysis. Chunduru et al. [13] selected CNNs
and Random Forests as classifiers, a decision likely influenced by the
robustness of these methods in handling high-dimensional data and complex
decision boundaries. Meanwhile, Gharaibeh et al. [17] conducted a thorough
review of various Deep Learning models alongside traditional machine learning
approaches, providing valuable insights into the comparative efficacy of
different methodologies. Moreover, Xv et al. [44] introduced a support vector
machine (SVM) coupled with feature selection algorithms, offering a nuanced
approach to feature engineering and model optimization. Conversely, Qadir et
al. [33] explored a hybrid model integrating DenseNet201 and Random Forest,
underscoring the trend towards hybrid architectures that hold the strengths of
multiple methodologies. Similarly, Chak et al. [10] endeavored to develop a
kidney image classifier using a combination of SVM and NN, showcasing the
synergy between traditional machine learning and deep learning approaches. On
the application side, Soni and Rai [36] focused on kidney stone detection,
employing Histogram Equalization and embossing techniques in conjunction with
SVM as the classifier. This highlights the importance of methodologies to
specific clinical challenges within the broader domain of kidney imaging. In a
similar, Alshebly et al. [4] utilized neural networks and Logistic Regression
for predicting chronic kidney disease, illustrating the diverse array of
predictive modeling techniques employed in the literature. The prevalence of
random forest classifiers in papers by Amiri et al. [6] and Alex et al. [2]
underscores their utility in handling complex datasets characteristic of
kidney imaging. Moreover, Alex et al. [2] introduced a modified hybrid random
forest classifier specifically for predicting kidney damage, demonstrating an
approach to algorithmic development.

Furthermore, Islam et al. [21] employed VGG16 and Swin Transformer for
detecting kidney stones, cysts, and tumors, showcasing the integration of
state-of-the-art deep learning architectures for comprehensive organ-level
analysis. Similarly, Pande et al. [30] implemented YoLo v8 for predicting
kidney abnormalities, highlighting the potential of advanced object detection
techniques in this domain. Finally, across papers by Reuben et al. [34],
Vasanthselvakumar et al. [40], Alzu’bi et al. [5], Parakh et al. [32], Kuo et
al. [26], Chaki and Ucar [11], Tushar et al. [38], and Majid et al. [28], deep
learning methods emerge as the predominant approach for addressing kidney
abnormality issues. This underscores the growing trend towards leveraging deep
learning’s capacity for representation learning and feature abstraction in
medical imaging tasks, particularly in the context of kidney disease diagnosis
and prognosis.

##  3 Proposed Feature Extraction Method

In this section, the proposed feature extraction method is presented. The aim
is to capture not only the background texture but also the inherent features
within CT scan images. Drawing inspiration from the Local Binary Pattern (LBP)
approach, a novel method termed Adaptive Local Binary Pattern (A-LBP) is
introduced. This section explains the details of the proposed feature
descriptor, the A-LBP method.

Figure 1: The encoding processes of LBP and A-LBP in a pixel block. Algorithm
1 Proposed Adaptive Binary Pattern (A-LBP) Algorithm

Input: Input image (matrix), Center pixel coordinates $(x_{c},y_{c})$ and
Adaptive parameter $\beta$

Output: Binary pattern corresponding to the center pixel and its decimal value

1: Let $I_{c}(x_{c},y_{c})$ represent the center pixel value of the image at
coordinates $(x_{c},y_{c})$ and neighborhood pixel value as $I_{n}$ and
Assigned value as $V_{n}$

2: Define upper and lower bounds as $UBV$ & $LBV$ where,
$UBV=I_{c}\times(1+\beta)$ snd $LBV=I_{c}\times(1-\beta)$

3: for each pixel in the 8-neighborhood of $(x_{c},y_{c})$ do

4: if $LBV\leq I_{n}\leq UBV$ then

5: Set $I_{n}$ = 1 = $V_{n}$

6: else

7: Set $I_{n}$ = 0 = $V_{n}$

8: end if

9: end for

10: Construct a binary pattern by traversing the neighborhood pixel values in
a clockwise direction, starting from the upper left as LSB to MSB.

11: Convert the binary pattern into a decimal value using
$\sum_{n=0}^{N-1}V_{n}\times 2^{n}$ equation.

12: Replace center pixel value $C(x,y)$ with acquired decimal value from the
binary pattern

The first process block in figure 1 demonstrates the operation of LBP. It
involves taking the center value of the pixel and comparing it with the values
of the 8 neighboring pixels. If a neighbor pixel value is greater than or
equal to the center pixel value, it is replaced with 1; otherwise, it is
replaced with 0. Next, a binary pattern is constructed, starting with the top-
left value as the least significant bit (LSB) and proceeding clockwise to the
value as the most significant bit (MSB). The binary pattern is then converted
into a decimal value, which is subsequently utilized to replace the center
pixel value. To understand the second process block in figure 1, the reference
to our proposed algorithm 1 provides further clarity.

To explain the second process block from the figure 1, we can check out the
equation 1.

$F_{\text{texture}}=\text{ALBP}_{((x_{c},y_{c}),\beta)}=\sum_{n=0}^{N-1}V_{n}\times
2^{n}$ (1)

In equation 1, $V_{n}$ denotes the value assigned to the $n^{th}$ neighboring
pixel based on a thresholding function. Then, the ALBP value is the decimal
value of the binary pattern. In ${ALBP}_{((x_{c},y_{c}),\beta)}$ for the
center pixel at coordinates $(x_{c},y_{c})$ and the adaptive value $\beta$ is
computed as a weighted sum of the binary values assigned to its neighboring
pixels. To determine the threshold function, let $I(x_{c},y_{c})$ denote the
intensity of the center pixel at coordinates $(x_{c},y_{c})$ in the input
image. The ALBP value for this pixel is computed based on the comparison of
its intensity with the intensities of its neighboring pixels within a defined
threshold range.The thresholding function, denoted as $T(I_{c},I_{n},\beta)$ ,
compares the intensity of the center pixel $I_{c}$ with the intensity of each
neighboring pixel $I_{n}$. The condition we put as the threshold range is
given in equations 2.

$V_{n}=\begin{cases}(1-\beta)\times{I_{c}}\leq{I_{n}}\leq(1+\beta)\times{I_{c}}&V_{n}=1\\\
otherwise&V_{n}=0\end{cases}$ (2)

##  4 Dataset

12,446 unique PACS images from different hospitals in Dhaka, Bangladesh were
included in the dataset [22]. The photos showed patients with kidney tumors,
cysts, normal conditions, or kidney stones. From contrast and non-contrast
investigations of the whole abdomen and urogram protocols, both coronal and
axial cuts were included. The DICOM data have been converted into lossless
JPEG. The author of the dataset mentioned that to guarantee data accuracy,
each image finding was verified by a medical technician. 3,709 photographs of
cysts, 5,077 images of normal circumstances, 1,377 images of kidney stones,
and 2,283 images of tumors.

##  5 Proposed Framework

In this section, the proposed framework has been discussed. Figure 2 is the
proposed architecture of our work.

Figure 2: Proposed Methodology

###  5.1 Data Preprocessing

####  5.1.1 Data Cropping & Resizing

The initial phase of our image processing workflow involves meticulously
cropping the CT scan images to isolate and emphasize the pertinent anatomical
structures. This cropping process serves a dual purpose: first, it effectively
eliminates extraneous background noise and non-essential elements from the
images, thus enhancing the clarity and focus on the regions of interest.
Second, it ensures that subsequent analyses are conducted with precision and
efficiency, as they are directed toward the critical anatomical features
essential for diagnosis and evaluation. Figure 3(a) is a raw dataset after
that it has been cropped as shown in figure 3(b).

Following the cropping stage, we proceed to standardize the resolution of the
images to a uniform size, typically resizing them into a standardized
dimension of 224x224 pixels. This standardization is crucial for several
reasons. Firstly, it facilitates consistency across the dataset, ensuring that
all images are represented in a comparable format, which is essential for
reliable and accurate analysis. Standardizing the resolution also helps to
streamline subsequent computational processes, as it reduces variability and
simplifies the integration of various image processing techniques and
algorithms. Figure 3 (c) is an image after resizing it into 224x224 pixels.

####  5.1.2 Apply Contrast-Limited Adaptive Histogram Equalization (CLAHE) to
the Data

CALHE, or Contrast Adaptive Local Histogram Equalization, is an innovative
technique that enhances image contrast by adjusting to local intensity
variations, unlike traditional global histogram equalization methods. By
considering the local context, CALHE proves particularly valuable for
identifying subtle anatomical structures and anomalies in medical images. Its
adaptive approach revolutionizes visualization, offering unprecedented
precision and detail, thus advancing both research and clinical practice in
radiology and beyond. Figure 3 (d) is an image after applying CLAHE which
enhances the image more than regular raw data.

Figure 3: (a) Raw Normal Kidney Image (b) Cropped Image (c) Resized Image (d)
CLAHE Applied Image

###  5.2 Feature Extraction

For feature extraction, we used one of the handcrafted texture analysis
methods named Local Binary Pattern compared with our proposed Feature
Descriptor named Adaptive Local Binary Pattern.

####  5.2.1 Local Binary Pattern (LBP)

One promising method in medical image analysis is the categorization of renal
illness using Local Binary Patterns (LBP). A texture descriptor called LBP
compares a pixel’s intensity to that of its neighboring pixels to extract
characteristics from a picture. LBP can identify textural patterns that
indicate pathological changes in renal tissue, which helps distinguish between
healthy and sick regions when it comes to the classification of kidney
disease. Accurate and automated diagnosis of kidney disorders can be
accomplished by integrating LBP features into classification algorithms, such
as support vector machines or neural networks. This allows for prompt
interventions and customized treatment plans. The types of textural features
that are obtained after applying a Local Binary Pattern are illustrated in
Figure 4.

####  5.2.2 Our Proposed Feature Extraction Method, Adaptive Local Binary
Pattern (A-LBP)

We present Adaptive Local Binary Pattern (A-LBP), a novel feature extraction
technique that aims to extract intrinsic features from CT scan images as well
as background texture. A-LBP provides a more sophisticated method that takes
local context into account, and it takes inspiration from Local Binary Pattern
(LBP). The algorithm and accompanying figure describe the A-LBP method, which
compares pixel intensities with neighboring pixels within a threshold range.
Each pixel block’s descriptor is the binary pattern that is produced after
being converted to a decimal value. A-LBP is a descriptor that captures both
structural and textural information. Through the use of A-LBP, physicians are
able to obtain a better understanding of the subtle differences found in
kidney images, which is important for correctly classifying healthy and
diseased tissue. Specifically, A-LBP captures nuanced texture patterns
indicative of renal pathologies, thereby facilitating automated diagnosis and
personalized treatment strategies. The features that are obtained after
applying the Adaptive Local Binary Pattern are illustrated in Figure 4
comparing with the Local Binary Pattern.

Figure 4: Four types of kidney image slices after applying Local Binary
Pattern (LBP) and our proposed feature descriptor, Adaptive Local Binary
Pattern(A-LBP)

###  5.3 Classifier Selection

The given features were utilized to train the classifiers listed below, and a
soft voting ensemble technique was proposed to improve the robustness of the
decision-making process for detecting the four types of kidney diseases.

####  5.3.1 Random Forest

As part of its ensemble learning process, Random Forest builds a large number
of decision trees during training and outputs a class that represents the mean
prediction (regression) or mode of the classes (classification) of each
individual tree. Because each tree in the random forest is constructed using a
random subset of the dataset’s features, overfitting is minimized and
robustness is raised. Random Forest gives more consistent and accurate results
by aggregating the predictions of several decision trees.

####  5.3.2 Decision Tree

A supervised learning technique called decision trees is applied to tasks
involving regression and classification. In order to maximize the homogeneity
of the target variable within each subset, it recursively partitions the
dataset into subsets based on the value of a chosen feature. Decision trees
are widely used because of their interpretability and simplicity in
understanding, as well as their capacity to capture intricate decision
boundaries.

####  5.3.3 Naive Bayes

The ”naive” assumption of feature independence underlies the probabilistic
classification technique known as Naive Bayes, which is based on the Bayes
theorem. Naive Bayes is a simple algorithm that works well in practice,
especially when it comes to text categorization jobs. Given a set of features,
it determines the likelihood of each class and uses the class with the highest
probability as the forecast.

####  5.3.4 K-Nearest Neighbor (K-NN)

Finding the majority class among a given data point’s k nearest neighbors is
how the straightforward and user-friendly K-Nearest Neighbor classification
technique operates. The class with the highest frequency among the neighbors
is allocated to the data point, and the choice of k sets the number of
neighbors considered. K-NN mostly depends on the distance metric that’s used
to gauge how similar two data points are to one another.

####  5.3.5 Support Vector Machine (SVM)

Provide assistance For classification and regression applications, Support
Vector Machine is a potent supervised learning algorithm. In order to divide
the data points of various classes with the largest margin possible, it finds
the ideal hyperplane. Because SVMs may handle nonlinear decision boundaries by
utilizing a variety of kernel functions, they are both effective and versatile
in high-dimensional spaces.

####  5.3.6 Soft Voting Ensemble

By averaging the probability assigned to each class, the Soft Voting Ensemble
is a meta-estimator classifier that aggregates the predictions of several
basic classifiers. It enables varied contributions from each classifier
according to its degree of confidence, leading to a more sophisticated
decision-making process. By utilizing the advantages of a variety of base
classifiers, a soft voting ensemble can enhance the classification task’s
overall performance and robustness.

##  6 Experimental Results

In this section, the experimental results that we have obtained from the
proposed project have been discussed.

###  6.1 Experimental Settings

The dataset was divided into 80% for training and 20% for testing across all
cases. Our proposed feature extraction method (Adaptive Local Binary Pattern
(A-LBP) ), along with Local Binary Pattern (LBP), was employed on the
preprocessed dataset. The classifiers mentioned in section 5.3 were utilized.
Evaluation metrics including precision, recall, and F1 Score were employed to
assess and compare the effectiveness of the models. No GPU was utilized for
training; instead, an Intel Xeon 2.20 GHz CPU with 32GB of RAM was employed.
Training and evaluation processes were conducted on Kaggle.

###  6.2 For Detecting the Normal Kidney Images

In order to identify kidney abnormalities of type Cyst in CT scan images, the
table compares the performance of two feature descriptors for classification:
Local Binary Pattern (LBP) and Adaptive Local Binary Pattern (ALBP). In terms
of test accuracy, precision, recall, and F1 score, ALBP consistently performs
better than LBP across a range of classifiers. For example, Random Forest
obtained a test accuracy of 91% using the LBP descriptor, whereas ALBP greatly
increased the accuracy to 97%. In a similar vein, ALBP outperformed LBP in
terms of precision, recall, and F1 scores for every classifier. Interestingly,
ALBP scored almost perfectly on most metrics, demonstrating its excellence in
identifying the complex traits associated with anomalies of the cystic kidney.
On the other hand, the performance of LBP varied greatly among classifiers,
with certain ones obtaining comparatively less precision and accuracy.

Kidney Type: Normal
---
| Feature
---
Descriptor
Classifier |  | Test
---
Accuracy
Precision | Recall | F1 Score
Local Binary Pattern (LBP) | Random Forest (RF) | .91 | .88 | .99 | .93
Decision Tree (DT) | .80 | .83 | .85 | .84
Naive Bayes (NB) | .53 | .66 | .47 | .55
K-Nearest Neighbor (K-NN) | .96 | .98 | .96 | .97
Support Vector Machine (SVM) | .89 | .85 | 1.00 | .92
Soft Voting Ensemble | .88 | .88 | .95 | .92
Adaptive Local Binary Pattern (ALBP) | Random Forest (RF) | .97 | .95 | 1.00 | .98
Decision Tree (DT) | .80 | .85 | .87 | .86
Naive Bayes (NB) | .68 | .76 | .81 | .78
K-Nearest Neighbor (K-NN) | .99 | 1.00 | 1.00 | 1.00
Support Vector Machine (SVM) | .99 | 1.00 | 1.00 | 1.00
Soft Voting Ensemble | .99 | .99 | 1.00 | 1.00

Table 1: Kidney Type: Normal

###  6.3 For Detecting the Stone in Kidney Images

The performance of classifiers based on the feature descriptor is highlighted
in the analysis of table 2, with an emphasis on finding the best descriptor to
recognize kidney stones. K-Nearest Neighbor (K-NN) is the best classifier
using the Local Binary Pattern (LBP) descriptor; it achieves a remarkable 96%
test accuracy and shows strong precision, recall, and an F1 score of 92%. On
the other hand, Naive Bayes (NB) shows a consistent lower score on all
criteria, suggesting that it is not very effective for this task. But the
descriptor that really stands out is the Adaptive Local Binary Pattern (ALBP)
one. Classifiers that use ALBP, like K-NN, SVM, and the Soft Voting Ensemble,
routinely achieve remarkable accuracy rates of 99% along with perfect
precision, recall, and F1 scores. Significantly, Naive Bayes consistently
demonstrates poor performance, confirming its inadequacy for precise kidney
stone categorization. Therefore, while both descriptors have their strengths,
the ALBP descriptor emerges as the superior choice for effectively identifying
kidney stones, as evidenced by the outstanding performance of classifiers
utilizing this feature descriptor.

Kidney Type: Stone
---
| Feature
---
Descriptor
Classifier |  | Test
---
Accuracy
Precision | Recall | F1 Score
Local Binary Pattern (LBP) | Random Forest (RF) | .91 | .98 | .69 | .81
Decision Tree (DT) | .80 | .62 | .61 | .62
Naive Bayes (NB) | .53 | .13 | .27 | .18
K-Nearest Neighbor (K-NN) | .96 | .92 | .92 | .92
Support Vector Machine (SVM): | .89 | 1.00 | .56 | .72
Soft Voting Ensemble | .88 | .86 | .69 | .76
Adaptive Local Binary Pattern (ALBP) | Random Forest (RF) | .97 | 1.00 | .84 | .92
Decision Tree (DT) | .80 | .59 | .61 | .60
Naive Bayes (NB) | .68 | .39 | .67 | .49
K-Nearest Neighbor (K-NN) | .99 | 1.00 | 1.00 | 1.00
Support Vector Machine (SVM) | .99 | 1.00 | .98 | .99
Soft Voting Ensemble | .99 | 1.00 | .97 | .98

Table 2: Kidney Type: Stone

###  6.4 For Detecting the Tumor in Kidney Images

ALBP-based classifiers routinely achieve superior performance compared to LBP-
based classifiers across multiple parameters in Table 3. For instance, ALBP-
driven Random Forest (RF) classifiers exhibit a remarkable test accuracy of
97%, coupled with a precision of 100%, recall of 97%, and an F1 score of 98%.
In contrast, LBP-based RF classifiers achieve a lower accuracy of 91%, along
with a precision of 95%, recall of 79%, and an F1 score of 86%. Similarly,
ALBP-based classifiers such as K-Nearest Neighbor (K-NN), Support Vector
Machine (SVM), and the Soft Voting Ensemble consistently demonstrate higher
accuracy rates and F1 scores compared to their LBP counterparts. This
consistent trend underscores the superiority of ALBP over LBP in accurately
identifying kidney tumors, making it a compelling choice for medical imaging
tasks requiring precise classification.

Kidney Type: Tumor
---
| Feature
---
Descriptor
Classifier |  | Test
---
Accuracy
Precision | Recall | F1 Score
Local Binary Pattern (LBP) | Random Forest (RF) | .91 | .95 | .79 | .86
Decision Tree (DT) | .80 | .71 | .68 | .69
Naive Bayes (NB) | .53 | .47 | .47 | .47
K-Nearest Neighbor (K-NN) | .96 | .93 | .94 | .93
Support Vector Machine (SVM) | .89 | .96 | .77 | .85
Soft Voting Ensemble | .88 | .91 | .71 | .80
Adaptive Local Binary Pattern (ALBP) | Random Forest (RF) | .97 | 1.00 | .97 | .98
Decision Tree (DT) | .80 | .75 | .75 | .75
Naive Bayes (NB) | .68 | .53 | .50 | .51
K-Nearest Neighbor (K-NN) | .99 | 1.00 | 1.00 | 1.00
Support Vector Machine (SVM) | .99 | 1.00 | 1.00 | 1.00
Soft Voting Ensemble | .99 | 1.00 | .97 | .98

Table 3: Kidney Type: Tumor

###  6.5 For Detecting the Cyst in Kidney Images

The table 4 compares the performance of different classifiers using Local
Binary Pattern (LBP) and Adaptive Local Binary Pattern (ALBP) feature
descriptors to detect cysts from kidney. A-LBP Performance was outstanding.
Using ALBP, the classifiers achieved high accuracy, with Random Forest and
Support Vector Machine (SVM) reaching 99% accuracy and a perfect 1.00 in
precision, recall, and F1 score. Both LBP and ALBP benefited from the soft
voting ensemble technique, showing improved robustness in classification but
with ALPB the results were remarkable and far better than what we had with
LPB.

Kidney Type: Cyst
---
| Feature
---
Descriptor
Classifier |  | Test
---
Accuracy
Precision | Recall | F1 Score
Local Binary Pattern (LBP) | Random Forest (RF) | .91 | .93 | .97 | .95
Decision Tree (DT) | .80 | .89 | .88 | .89
Naive Bayes (NB) | .53 | .73 | .73 | .73
K-Nearest Neighbor (K-NN) | .96 | .96 | .97 | .96
Support Vector Machine (SVM): | .89 | .93 | .96 | .85
Soft Voting Ensemble | .88 | .88 | .96 | .92
Adaptive Local Binary Pattern (ALBP) | Random Forest (RF) | .97 | .98 | .99 | .99
Decision Tree (DT) | .80 | .85 | .82 | .84
Naive Bayes (NB) | .68 | .88 | .60 | .71
K-Nearest Neighbor (K-NN) | .99 | 1.00 | 1.00 | 1.00
Support Vector Machine (SVM): | .99 | .99 | 1.00 | 1.00
Soft Voting Ensemble | .99 | 1.00 | 1.00 | 1.00

Table 4: Kidney Type: Cyst

##  7 Discussion

Table 5 showcases a comparative analysis between existing studies on kidney
disease diagnosis and our research. Our model has made significant
advancements in the field, surpassing the performance of previous models and
making notable contributions to the domain. Our approach, which uses an
ensemble of five models (Random Forest, Decision Tree, Naive Bayes, KNN, and
SVM) along with Adaptive Local Binary Pattern (A-LBP), achieves an impressive
accuracy of 0.99 when compared to existing kidney stone detection methods.
This accuracy level matches, if not exceeds, several of the well-known
techniques covered in the table 5. Unlike some other approaches that
concentrate on certain classifiers or algorithms, such as CNN, our strategy
makes use of the synergy between numerous models, utilizing their strengths to
improve overall performance. Furthermore, by using A-LBP, our approach shows
flexibility to local characteristics, which is important for correctly
detecting kidney abnormalities in a variety of complicated datasets. Overall,
the kidney stone detection capabilities of our suggested technique are
promising and have the potential to be robust in medical imaging.

Authors | Method | Algorithm/Classifier Used | Dataset | Accuracy
---|---|---|---|---
Mehmet et al.[7] | Exemplar Darknet | KNN |  | 1799 CT images
---
\- 790 kidney stones
\- 1009 healthy classes
0.99
Vasanthselvakumar R et al.[40] | HoG, CNN | Adaboost Classifier |  | 90 ultrasound images
---
\- 91 kidney stones
\- 92 Cystic Kidney
\- 87 Kidney Cancer
| CNN - 0.85
---
SIFT with SVM - 0.82
SURF with SVM - 0.84
|  Chelladurai CALLINS CHRISTIYANA et al.[12]
---
LFLBP | - |  | 365 images
---
-Normal,
\- Cortical Cysts
\- Medical Renal Diseases
| Normal – 0.81
---
CC – 0.63
MRD – 0.86
Jyoti Verma et al.[41] | PCA | KNN, SVM |  | \- Abnormal Kidney
---
-Normal Kidney
| KNN - 0.89
---
SVM - 0.84
Anilkumar Chunduru[13] | CNN |  | Logistic Regression,
---
XGBoost,
KNN,
Navie Bayes,
SVM
| \- 3889 infected Kidney
---
\- 1349 Normal
| Logistic Regression – 0.76,
---
XGBoost – 0.72,
KNN - 0.70,
Navie Bayes- 0.71,
SVM - 0.75
Kadir et.al[45] | - | XResNet-50 |  | 1799 images
---
\- Normal
\- Stone
0.96
Abdalbasit Mohammed Qadir[33] | DenseNet201 | Random Forest |  | 5400 CT images
---
\- 1350 Cystic Kidney
\- 1350 Normal Kidney
\- 1350 Stone Kidney
\- 1350 Tumor Kidney
0.99
Sudharson et.al[37] |  | ResNet-101, ShuffleNet,
---
and MobileNet-v2
SVM |  | 4420 CT images
---
\- 1105 Cystic Kidney
\- 1105 Normal Kidney
\- 1105 Stone Kidney
\- 1105 Tumor Kidney
0.96
Dalia et.al[5] | Canny Edge Detection |  | VGG16,
---
ResNet50,
2D CNN
| 8600 CT images
---
\- Normal
\- Tumor
| VGG16- 0.60
---
ResNet50 - 0.96,
2D CNN – 0.97
Md Nazmul et.al[21] | - |  | EANET,
---
Swin Transformers,
CCT,
VGG16,
Inception V3,
Resnet50.
| 12446 CT images
---
\- 3709 Cystic Kidney
\- 5077 Normal Kidney
\- 1377 Stone Kidney
\- 2283 Tumor Kidney
| EANET - 0.77,
---
Swin Transformers - 0.99,
CCT - 0.96,
VGG16 - 0.98,
Inception V3 - 0.61,
Resnet50 - 0.73.
Our Proposed Method |  | Adaptive Local Binary Pattern
---
(A-LBP)
| Ensemble five models
---
(Random Forest,
Decision Tree,
Naïve Bayes,
KNN,
SVM)
| 12446 CT images
---
\- 3709 Cystic Kidney
\- 5077 Normal Kidney
\- 1377 Stone Kidney
\- 2283 Tumor Kidney
0.99

Table 5: Performance metrics comparison of other models and our proposed
method.

##  8 Novelty

Several innovative contributions to the categorization of kidney anomalies in
CT scan pictures are presented by this effort. The main contribution is the
creation of the Adaptive Local Binary Pattern (ALBP) feature descriptor, which
is designed to distinguish between different kidney textures. In contrast to
traditional techniques, our method innovates by creating a custom description,
leading to significant gains in all assessment criteria. Furthermore, new uses
of picture preprocessing methods like Contrast-Limited Adaptive Histogram
Equalization (CLAHE) improve our classification accuracy even further. By
implementing these advances, we are able to identify kidney problems in CT
scan pictures with greater accuracy. Also to run this program a person doesn’t
need a GPU which is environment-friendly and it can run on a regular machine.

##  9 Conclusion & Future Work

The Adaptive Local Binary Pattern (ALBP) feature descriptor improves the
classification of kidney anomalies in medical CT scan pictures, according to
the study’s conclusion. A unique custom descriptor was created to distinguish
across different kidney textures, and this led to notable gains in all
assessment criteria. It will make it easier for the medical professional to
identify kidney abnormalities. This study emphasizes how crucial innovative
feature extraction techniques are to raising the accuracy of medical picture
analysis. Upgrading preprocessing methods, investigating sophisticated machine
learning algorithms, and verifying results on larger datasets could be the
main goals of future work to raise the bar for kidney anomaly categorization.

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