Deep Learning Techniques for Colorectal Cancer Detection: Convolutional Neural Networks vs Vision Transformers

Colorectal cancer (CRC) is one of the most common cancers among humans, its diagnosis is made through the visual analysis of tissue samples by pathologists; artificial intelligence (AI) can automate this analysis based on histological images generated from different tissue samples. In this paper we aim to enhance this digital pathology process by proposing two deep learning (DL) based methods that are extremely accurate and reliable despite several limitations. Our first method is based on Convolutional Neural Networks (CNN) in order to classify different classes of tissues into cancerous and non-cancerous cells based on histological images. Our second method is based on Vision Transformers and also classifies images into cancerous and non cancerous cells. Due to the sensitivity of the problem, the performance of our work will be estimated using accuracy, precision, recall and F-score metrics since they ensure more credibility to the classification results; our models have been tested and evaluated with a dataset collected from LC25000 database containing 10000 images of cancerous and non-cancerous tissues, our models achieved promising results with an overall accuracy of 99.84% and 98.95% respectively with precision= 100%, recall= 100% and F1-score= 100%, we observed that both of our models overcame several state-of-the-art results.