Title: Deep learning approach using modified DarkNet-53 for renal cell carcinoma grading

Authors: G. Sathish Kumar; G. Uma Maheshwari; C. Selvan; M. Nagasuresh; D. Rasi; P. Swathypriyadharsini; Sathish Kumar Danasegaran

Addresses: Centre for Computational Imaging and Machine Vision, Department of Artificial Intelligence and Data Science, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India ' Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, Coimbatore, Tamil Nadu, India ' School of Computer Science and Engineering, REVA University, Bangalore, Karnataka, India ' Department of Information Technology, Karpagam Institute of Technology, Coimbatore, Tamil Nadu, India ' Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India ' Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Erode, Tamil Nadu, India ' Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

Abstract: 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.

Keywords: modified DarkNet; convolutional neural network; CNN; ensemble deep learning; kidney cancer; renal cell carcinoma; RCC; whole slide images; WSIs.

DOI: 10.1504/IJBRA.2025.144023

International Journal of Bioinformatics Research and Applications, 2025 Vol.21 No.1, pp.1 - 25

Received: 17 Oct 2023
Accepted: 19 Feb 2024
Published online: 21 Jan 2025 *

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