Deep Transfer Learning for Kidney Disease Classification Using Fine-Tuned ResNet50
Keywords:
Kidney Disease Classification, Deep Learning, ResNet50, Transfer Learning, Fine-Tuning, Medical Image Analysis, Ultrasound Imaging, Computer-Aided Diagnosis.Abstract
Cystic kidney diseases, kidney stones, and tumors are among the top contributors to the global burden of chronic kidney disease. Medical imaging plays a source role in the early and accurate detection of such abnormalities, which is critical to planning effective treatment courses. AbstractIn this paper, we propose a deep learning-based kidney disease classification system using a fine-tuned type of ResNet50 architecture. A dataset of a total of 12,446 ultrasound kidney images divided into four categories (Cyst, Normal, Stone, Tumor). This was used to train a model and validate it The baseline test accuracy achieved was 71.03%, and this was obtained by freezing and training the pretrained ResNet50 model using transfer learning. By then conducting further fine-tuning of the last 50 convolutional layers, model performance was improved to give a final test accuracy of 92.36%. The proposed solution is highly robust: precision and recall values are above 0.90 for most of the classes, indicating that this approach can help automate kidney disease diagnosis. The model also demonstrated it potential of real clinical applications in medical imaging diagnosis with comparative evaluation and ROC analysis.
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