Leveraging Nature-Inspired Search to Boost CNN Performance in Colorectal Cancer Classification | ||
| International Journal of Telecommunications | ||
| Volume 05, Issue 02, July 2025, Pages 1-20 PDF (2.08 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijt.2025.411907.1130 | ||
| Authors | ||
| Islam A. A. Mohammed* 1; Nour S. Bakr2; Hossam Moustafa3; Adel F. Mohamed Moustafa4; Hanan M. Amer5 | ||
| 1Program of Medical and Biological Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt. | ||
| 2Biomedical Engineering Department, Higher Technological Institute, 10th of Ramadan City, El Sharkia, Egypt. | ||
| 3Department of Electronics and Communications Engineering at the Faculty of Engineering, Mansoura Uni-versity | ||
| 4Oncology Center, Mansoura University, Mansoura 35516, Egypt. | ||
| 5Electronics 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 | ||
| Keywords: Histopathological; Colorectal Cancer (CRC); Convolutional Neural Network (CNN); Manta Ray Foraging Optimization (MRFO); Transfer Learning (TL) | ||
|
Statistics Article View: 39 PDF Download: 16 |
||
