Diagnosis Aid System for Colorectal Cancer Using Low Computational Cost Deep Learning Architectures

• Published in: Electronics (Basel) Vol. 13; no. 12; p. 2248
• Main Authors: Gago-Fabero, Álvaro, Muñoz-Saavedra, Luis, Civit-Masot, Javier, Luna-Perejón, Francisco, Rodríguez Corral, José María, Domínguez-Morales, Manuel
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Accuracy; Artificial neural networks; Cancer; Classification; Classifiers; Colorectal cancer; Computational efficiency; Computing costs; Datasets; Deep learning; Diagnosis; Economic aspects; Fatalities; Health aspects; Image resolution; Machine learning; Medical imaging; Medical imaging equipment; Medical personnel; Neural networks; Oncology; Tumors; Spain
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics13122248

Colorectal cancer is the second leading cause of cancer-related deaths worldwide. To prevent deaths, regular screenings with histopathological analysis of colorectal tissue should be performed. A diagnostic aid system could reduce the time required by medical professionals, and provide an initial approach to the final diagnosis. In this study, we analyze low computational custom architectures, based on Convolutional Neural Networks, which can serve as high-accuracy binary classifiers for colorectal cancer screening using histopathological images. For this purpose, we carry out an optimization process to obtain the best performance model in terms of effectiveness as a classifier and computational cost by reducing the number of parameters. Subsequently, we compare the results obtained with previous work in the same field. Cross-validation reveals a high robustness of the models as classifiers, yielding superior accuracy outcomes of 99.4 ± 0.58% and 93.2 ± 1.46% for the lighter model. The classifiers achieved an accuracy exceeding 99% on the test subset using low-resolution images and a significantly reduced layer count, with images sized at 11% of those used in previous studies. Consequently, we estimate a projected reduction of up to 50% in computational costs compared to the most lightweight model proposed in the existing literature.