Deep Learning-Assisted Techniques for Detection and Prediction of Colorectal Cancer From Medical Images and Microbial Modality

In the past decade, significant progress has been made in the fields of artificial intelligence, machine learning, and deep learning (DL). These advancements have opened up wide applications and opportunities in the medical field. Colorectal cancer (CRC) has gained substantial interest from researchers due to its ranking as the third most prevalent cancer type after breast and lung cancer, affecting around 10% of all cancer patients globally each year. It is the second leading cause of cancer-related death worldwide, making the development of efficient computer-assisted methods for its detection, prediction, and treatment crucial. There are modalities used for colorectal cancer screening and detection such as colonoscopy images, biopsy samples, biomarker data, blood samples, CT scan, MRI, ultrasound, PET, and microbial data. The advancement of technology has made deep learning an attractive choice for fast and effective detection, segmentation, and prediction of diseases through image analysis. This technology has the potential to assist and empower medical professionals in making timely and informed decisions. Deep learning has proven to be highly effective when ample high-quality features and large datasets are available. However, one of the main challenges in using deep learning for medical image analysis is the limited availability of datasets from medical centers. This chapter provides an overview of DL-based models and their application in detecting and predicting CRC from various modalities. On the SCPolyps dataset the OEM model achieved training and test accuracy of 98% and 96% respectively.