Open Access

ARTICLE

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

by Ali Altalbe^1,2,*, 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:

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

Received 25 March 2023; Accepted 23 May 2023; Issue published 28 July 2023

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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Keywords

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