European Journal of Molecular & Clinical Medicine

Abstract

Colorectal cancer is a well-known tumour that affects both men and women across the world and is quite common. According to a study published by the World Health Organization in 2018, colon cancer ranked third, with 1.80 million people afflicted. To be more specific, it is the cancer that comes after it that is the second most frequent cause of cancer in women and the third most common cause of cancer in men. Colorectal cancer is thought to be caused by a lack of control over the integrity of epidermal cells, which may occur in the intestine or during a malignancy. A reliable method of detecting colon cancer at an early stage, followed by intensive treatment, has the potential to significantly lower the mortality rates that result. A Gastroenterologist may resort to cancer diagnostic tests for pathological pictures in order to do Screening of Morphology of Malignant Tumor Cells in the Colon during a colonoscopy. Due to the unlimited number of glands in the gastrointestinal system, any Histology procedure will require a large amount of time, and the results may be incongruous. By diagnosing using computer algorithms, it is possible to get practical and beneficial outcomes.. In order to get trustworthy and useful morphological imaging data, correct gland segmentation is a critical pre-processing step that must be completed first. In recent years, researchers have used deep learning algorithms to pathological image analysis in order to improve the accuracy of cancer illness detection. According to our findings, diagnostic test characteristics that are provided as input to a deep learning architecture that is utilized in conjunction with a semantic segmentation algorithm may provide results that are more accurate than those produced by conventional picture segmentation methods. This paper presents an in-depth examination of deep learning architectures used for semantic segmentation on histological pictures of the colon, as well as their applications.