Kidney X-ray Images Classification using Machine Learning and Deep Learning Methods

Aksakalli, Isil Aarabey; Kaçdioğlu, Sibel; Hanay, Y. Sinan

doi:10.17694/bajece.878116

Cited by 27 publications

(12 citation statements)

References 28 publications

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“…Several ML algorithms have been investigated for kidney image classification, including Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Naive Bayes, and KNN [ 18 ]. The best results were obtained using the KNN and Naive Bayes classifiers.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The first CNN model for CT image classification and feature reduction was AlexNet, whereas the second model was U-Net, which accurately segmented the kidneys. In [ 18 , 33 ], kidney ultrasound images were analysed using pre-trained DNN models, mainly ResNet-101, ShuffleNet, and MobileNet-v2, for feature extraction, and an SVM, for the classification. The above review indicates that research on kidney images can be divided into two main categories: one focuses on kidney image segmentation, and the other focuses on diagnosing kidney disorders.…”

Section: Literature Reviewmentioning

confidence: 99%

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Feature Extraction Based on Local Histogram with Unequal Bins and a Recurrent Neural Network for the Diagnosis of Kidney Diseases from CT Images

Gharahbagh,

Hajihashemi,

Machado

et al. 2024

Bioengineering

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Kidney disease remains one of the most common ailments worldwide, with cancer being one of its most common forms. Early diagnosis can significantly increase the good prognosis for the patient. The development of an artificial intelligence-based system to assist in kidney cancer diagnosis is crucial because kidney illness is a global health concern, and there are limited nephrologists qualified to evaluate kidney cancer. Diagnosing and categorising different forms of renal failure presents the biggest treatment hurdle for kidney cancer. Thus, this article presents a novel method for detecting and classifying kidney cancer subgroups in Computed Tomography (CT) images based on an asymmetric local statistical pixel distribution. In the first step, the input image is non-overlapping windowed, and a statistical distribution of its pixels in each cancer type is built. Then, the method builds the asymmetric statistical distribution of the image’s gradient pixels. Finally, the cancer type is identified by applying the two built statistical distributions to a Deep Neural Network (DNN). The proposed method was evaluated using a dataset collected and authorised by the Dhaka Central International Medical Hospital in Bangladesh, which includes 12,446 CT images of the whole abdomen and urogram, acquired with and without contrast. Based on the results, it is possible to confirm that the proposed method outperformed state-of-the-art methods in terms of the usual correctness criteria. The accuracy of the proposed method for all kidney cancer subtypes presented in the dataset was 99.89%, which is promising.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Feature Extraction Based on Local Histogram with Unequal Bins and a Recurrent Neural Network for the Diagnosis of Kidney Diseases from CT Images

Gharahbagh,

Hajihashemi,

Machado

et al. 2024

Bioengineering

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show abstract

“…In addition, a limited dataset is used, which limits the model's generalizability. Aksakalli et al ( 2 ) developed a DL model that detects whether a kidney X-ray image is patient or healthy. The proposed method comprises six phases: scaling, resizing, gray-level values extraction, generating CSV, resampling, and evaluation.…”

Section: Related Studiesmentioning

confidence: 99%

“…Kidney stones are one of the most common contributing factors to kidney function loss and, if left untreated, can lead to chronic kidney disease (CKD) development ( 1 ). Kidney stones are a common health problem that affects 1–15% of the world's population and is becoming more common with each passing year ( 2 ). For example, every year, over two million people in the USA seek treatment at an emergency department for renal colic or stone-related back pain ( 3 ).…”

Section: Introductionmentioning

confidence: 99%

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A two-stage renal disease classification based on transfer learning with hyperparameters optimization

et al. 2023

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Renal diseases are common health problems that affect millions of people around the world. Among these diseases, kidney stones, which affect anywhere from 1 to 15% of the global population and thus; considered one of the leading causes of chronic kidney diseases (CKD). In addition to kidney stones, renal cancer is the tenth most prevalent type of cancer, accounting for 2.5% of all cancers. Artificial intelligence (AI) in medical systems can assist radiologists and other healthcare professionals in diagnosing different renal diseases (RD) with high reliability. This study proposes an AI-based transfer learning framework to detect RD at an early stage. The framework presented on CT scans and images from microscopic histopathological examinations will help automatically and accurately classify patients with RD using convolutional neural network (CNN), pre-trained models, and an optimization algorithm on images. This study used the pre-trained CNN models VGG16, VGG19, Xception, DenseNet201, MobileNet, MobileNetV2, MobileNetV3Large, and NASNetMobile. In addition, the Sparrow search algorithm (SpaSA) is used to enhance the pre-trained model's performance using the best configuration. Two datasets were used, the first dataset are four classes: cyst, normal, stone, and tumor. In case of the latter, there are five categories within the second dataset that relate to the severity of the tumor: Grade 0, Grade 1, Grade 2, Grade 3, and Grade 4. DenseNet201 and MobileNet pre-trained models are the best for the four-classes dataset compared to others. Besides, the SGD Nesterov parameters optimizer is recommended by three models, while two models only recommend AdaGrad and AdaMax. Among the pre-trained models for the five-class dataset, DenseNet201 and Xception are the best. Experimental results prove the superiority of the proposed framework over other state-of-the-art classification models. The proposed framework records an accuracy of 99.98% (four classes) and 100% (five classes).

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Detection of Kidney Stone Based on Super Resolution Techniques and YOLOv7 Under Limited Training Samples

Nguyen,

Le,

Duong

et al. 2023

Lecture Notes on Data Engineering and Communications Technologies

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Kidney X-ray Images Classification using Machine Learning and Deep Learning Methods

Cited by 27 publications

References 28 publications

Feature Extraction Based on Local Histogram with Unequal Bins and a Recurrent Neural Network for the Diagnosis of Kidney Diseases from CT Images

Feature Extraction Based on Local Histogram with Unequal Bins and a Recurrent Neural Network for the Diagnosis of Kidney Diseases from CT Images

A two-stage renal disease classification based on transfer learning with hyperparameters optimization

Detection of Kidney Stone Based on Super Resolution Techniques and YOLOv7 Under Limited Training Samples

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