Multi-model deep learning approach for the classification of kidney diseases using medical images

Abstract

Renal impairment poses a risk across all ages. With the global nephrologist shortage, the rising public health concerns over kidney failure, and advancements in technology, there is a growing need for an AI system capable of identifying kidney anomalies automatically. Chronic kidney disease is marked by a gradual failure in kidney function due to various factors, such as stones, cysts, and tumors. Chronic kidney disease often presents without noticeable symptoms initially, leading to cases remaining untreated until advanced stages. Tumors, which are dense tissue masses, can directly harm organs, including glands and spinal cells. Kidney stone disease, or urolithiasis, occurs when many solids accumulate in the urinary tract, leading to stone formation. This research paper leveraged a deep learning approach to address the worldwide shortage of urologists by facilitating the detection of kidney diseases. A novel deep learning technique is proposed using Darknet53 for the classification of kidney diseases using a large dataset gathered from five resources. The total number of images is 27,145 scans of the entire abdomen and urogram, focusing on common kidney conditions, including stones, cysts, and tumors. The data was grouped into four classes: normal, cyst, tumor, and stone. The proposed technique involves the use of 16 deep-learning models to obtain enhanced performance based on accuracy, recall, specificity, and precision, offering new potential for detecting kidney abnormalities. Model performance was evaluated, achieving 99.69%, 0.31%, 99.66%, 99.88%, 99.77%, 0.12%, 99.71%, 99.60%, and 99.17% for accuracy, error, recall, specificity, precision, false positive rate, F1_score, Matthews Correlation Coefficient, and Kappa, respectively. Our simulation results using the Fuzzy Decision by Opinion Score Method indicated that the Darknet53 generated the best results for detecting kidney abnormalities.

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