Deep learning approach using modified DarkNet-53 for renal cell carcinoma grading
G. Sathish Kumar, G. Uma Maheshwari, Chella Keerthana Selvan, M. Nagasuresh, D. Rasi, P. Swathypriyadharsini, Sathish Kumar Danasegaran · International Journal of Bioinformatics Research and Applications · 2025
Accurate and effective diagnostic procedures are required for appropriate treatment planning for renal cell carcinoma, the most frequent form of kidney cancer. Using fusion module, a network dubbed modified DarkNet (MDNet) was developed for image-based small-target detection. We built MDNet on top of a modified version of DarkNet-53, which itself a scale matching approach, to increase its speed and accuracy. By combining the results of several convolutional neural network (CNN) models, the ensemble structure improves classification accuracy. The effectiveness of a classification algorithm using kidney histopathology pictures dataset is measured in accuracy, precision, recall, sensitivity, specificity and F1-score. The results show that the ensemble deep learning method outperforms both standalone CNN models and more conventional machine learning techniques in RCC classification. Overall grade classification accuracy of 98.9%, a sensitivity of 98.2%, and a high classification specificity of 98.7%, in distinguishing tissues.