Each year, more than 1.9 million cases of colorectal cancer (CRC) are diagnosed worldwide. By 2040, the burden of colorectal cancer is estimated to reach 3.2 million new cases per year and 1.6 million deaths per year worldwide. As of 2024, it ranks as the third most prevalent form of cancer, contributing to over 10% of all new cancer cases annually, with a 5-year survival rate of only 65%. With effective early detection mechanisms in place, the survival rate could potentially increase to 90%. However, current detection mechanisms are manual and error-prone.
This study presents a deep learning-based approach to automating the detection of polyps, the tumor that causes colorectal cancer, in the human colon. Various state-of-the-art deep learning models  – including VGG, ResNet, DenseNet, and EfficientNet were trained and tested on a publicly available dataset. The findings of this study show that deep learning models can significantly automate the early diagnosis process of colorectal cancer with high accuracy, especially the DenseNet and EfficientNet models – attaining 99% and 99.4% respectively for both accuracy and F1 score metrics on the test dataset. This study validates the potential of deep learning to enhance the accuracy and reliability of colorectal cancer detection and prevention, ultimately contributing to better quality of diagnosis and patient outcomes.