Document Type : Original Article
Authors
1
Program of Medical and Biological Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt.
2
Biomedical Engineering Department, Higher Technological Institute, 10th of Ramadan City, El Sharkia, Egypt.
3
Department of Electronics and Communications Engineering at the Faculty of Engineering, Mansoura Uni-versity
4
Oncology Center, Mansoura University, Mansoura 35516, Egypt.
5
Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt. Faculty of Engineering, Mansoura National University, Mansoura, Egypt
Abstract
Abstract: Cancer affecting the colorectal region (CRC) is a leading contributor to Worldwide cancer-associated mortality, where timely and precise diagnosis is critical for enhancing patient prognosis. Although histopathology remains the benchmark for diagnostic accuracy, its manual assessment is time-consuming and subject to variability among pathologists. To address these challenges, this paper proposes a novel optimization framework CrcMRFA based on the Manta Ray Foraging Optimization (MRFO) algorithm to fine-tune Convolutional neural nets initial-ized with learned weights from prior training for histopathological image classification. Three architectures—VGG16, ResNet50, and DenseNet121—were optimized with respect to key hy-perparameters, encompassing parameters such as learning rate, batch size, dropout rate, and the number of trainable layers. Experimental evaluation Leveraging the Kather_texture_2016_image_tiles_5000 dataset demonstrated significant performance en-hancements across all metrics. The optimized ResNet50 achieved the best results, with accuracy improving from 90.32% to 95.97% and the Weighted Sum Metric (WSM) exceeding 96.77%. These findings highlight the potential of MRFO in automating CNN optimization for robust and ef-ficient CRC tissue classification.
Keywords
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