Merve Esra Taşcı, Zahra Elmi, Ömer Faruk Albayrak, Mustafa Tokat

Proceedings of the 28th World Multi-Conference on Systemics, Cybernetics and Informatics: WMSCI 2024, pp. 92-98 (2024); https://doi.org/10.54808/WMSCI2024.01.92

Merve Esra Taşcı
Software Engineering Department, Sabahattin Zaim University, Istanbul, Turkey

Zahra Elmi
Computer Engineering Department, Sabahattin Zaim University, Istanbul, Turkey

Ömer Faruk Albayrak
Computer Engineering Department, Sabahattin Zaim University, Istanbul, Turkey

Mustafa Tokat
Computer Engineering Department, Sabahattin Zaim University, Istanbul, Turkey

Cite this paper as:
Taşcı, M. E., Elmi, Z., Albayrak, &. F., Tokat, M. (2024). Colorectal Cancer Diagnosis with Deep Learning Models. In N. Callaos, E. Gaile-Sarkane, N. Lace, B. Sánchez, M. Savoie (Eds.), Proceedings of the 28th World Multi-Conference on Systemics, Cybernetics and Informatics: WMSCI 2024, pp. 92-98. International Institute of Informatics and Cybernetics. https://doi.org/10.54808/WMSCI2024.01.92

The third most common disease in the world, colorectal cancer, frequently has the highest death rate. Surgery is a viable treatment option, but after five years, thirty to forty percent of patients have recurrence. Many people who have effectively treated their colorectal cancer also develop metastatic illness. Early detection is crucial since colorectal cancer has a high fatality rate. Deep learning techniques make colorectal cancer screening timelier and more costeffective by enabling early and quicker identification of the disease. A collection of cell pictures was employed in the study to detect colorectal cancer. To demonstrate the capability of deep learning approaches, we used Convolutional Neural Networks (CNN), AlexNet, VGG- 16, ResNet models and our proposed model as Hybrid CNN-LSTM. The accuracy and loss rates provided by the propos models were compared. The highest accuracy rate performance was observed with from the Hybrid CNNLSTM model. The highest loss rate performance was observed with from the CNN model.