Deep Learning Model Based Decision Support System for Kidney Cancer on Renal Images
Abstract
Kidney cancer remains one of the most lethal urological malignancies due to its lack of early symptoms and effective screening strategies. Timely diagnosis is crucial for improving treatment outcomes and patient survival. This study explores the application of deep learning models—EfficientNet-B1 and MobileNet-V2—for the classification of kidney abnormalities, including cysts, stones, tumors, and normal conditions, using computed tomography (CT) images. A dataset of 12,446 annotated kidney CT images was used for training and evaluation, with preprocessing techniques such as Gaussian filtering, Otsu’s binarization, and watershed segmentation enhancing image quality. Both models were implemented and compared based on accuracy, precision, recall, F1-score, and specificity. The EfficientNet-B1 model achieved superior performance with an overall accuracy of 99.27%, demonstrating strong precision and recall across all categories. MobileNet-V2 also performed well, attaining 96.81% accuracy, though with slightly reduced precision for stone detection. The results highlight the potential of transfer learning-based CNN architectures in assisting radiologists with reliable, automated kidney disease classification, thereby contributing to earlier detection and improved patient care.
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