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Applying Customized Convolutional Neural Network to Kidney Image Volumes for Kidney Disease Detection

Ali Altalbe1,2,*, Abdul Rehman Javed3

1 Department of Computer Science, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
2 Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
3 Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon

* Corresponding Author: Ali Altalbe. Email: email

Computer Systems Science and Engineering 2023, 47(2), 2119-2134. https://doi.org/10.32604/csse.2023.040620

Abstract

Kidney infection is a severe medical issue affecting individuals worldwide and increasing mortality rates. Chronic Kidney Disease (CKD) is treatable during its initial phases but can become irreversible and cause renal failure. Among the various diseases, the most prevalent kidney conditions affecting kidney function are cyst growth, kidney tumors, and nephrolithiasis. The significant challenge for the medical community is the immediate diagnosis and treatment of kidney disease. Kidney failure could result from kidney disorders like tumors, stones, and cysts if not often identified and addressed. Computer-assisted diagnostics are necessary to support clinicians’ and specialists’ medical assessments due to the rising prevalence of chronic renal illness, the lack of experts, and the rising rates of assessment and monitoring, mainly in developing nations. Artificial Intelligence (AI) approaches such as machine, and deep learning has been used in literature for kidney disease detection; however, they still lack performance. This paper implements a deep learning-based Convolutional Neural Network (CNN) model for the classification and prognosis of kidney disease. We use a benchmark Computed Tomography (CT) kidney dataset for experimentation. The data is pre-processed, and then CNN extracts the features from the images. Results reveal that the proposed approach accurately classifies kidney disease with a considerable accuracy of 0.992%, 0.994% precision, 0.982% recall, and 0.987% F1-score. This study suggests using the proposed fine-tuned CNN model for kidney disease detection.

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APA Style
Altalbe, A., Javed, A.R. (2023). Applying customized convolutional neural network to kidney image volumes for kidney disease detection. Computer Systems Science and Engineering, 47(2), 2119-2134. https://doi.org/10.32604/csse.2023.040620
Vancouver Style
Altalbe A, Javed AR. Applying customized convolutional neural network to kidney image volumes for kidney disease detection. Comput Syst Sci Eng. 2023;47(2):2119-2134 https://doi.org/10.32604/csse.2023.040620
IEEE Style
A. Altalbe and A.R. Javed, "Applying Customized Convolutional Neural Network to Kidney Image Volumes for Kidney Disease Detection," Comput. Syst. Sci. Eng., vol. 47, no. 2, pp. 2119-2134. 2023. https://doi.org/10.32604/csse.2023.040620



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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