A Hybrid Deep Learning Approach to Classify Colon Cancer from Colonoscopy Image

These days, colon cancer is a frightening global problem that kills many people. Particularly in countries in South Asia, it is growing daily. Because early discovery can significantly affect the cancer’s survival. An AI and machine learning integration technique must be developed to do this. Our main goal is to use cutting-edge machine-learning algorithms to detect colon cancer. 6000 raw pictures of colon cancer in four classes—Normal, U1cerative_Colits, Polyps, and Esophagitis— were used in this study. After applying image pre-processing techniques to improve the data quality, we implemented three advanced deep-learning techniques using our Customized model. Our customized model (InceptionResNetV2 + customized layers) provides the maximum accuracy of 95.88%, whereas VGG19, Inception v3, and ResNet50 provide healthy accuracy. We have performed a comparative analysis with related work by those who work with the same dataset, and our model outperformed all of them. Our suggested model, preprocessing strategies, and data verification approach demonstrate an influential body of work in the medical field. Due to its advanced AI and ML-based research, this could have a significant impact on the computerized medical sector.