Int J Cur Res Rev | Vol 12 • Issue 22 • November 2020 150 Corresponding Author: Professor Tai-hoon Kim, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Field, Vaddeswaram, Guntur 522502, India; Email: taihoonn@daum.net ISSN: 2231-2196 (Print) ISSN: 0975-5241 (Online) Received: 18.08.2020 Revised: 20.09.2020 Accepted: 25.10.2020 Published: 24.11.2020 Research Article International Journal of Current Research and Review DOI: http://dx.doi.org/10.31782/IJCRR.2020.122234 INTRODUCTION The occurrence and fatality percentage of colorectal malig- nancy has much increased in contemporary years. More of- ten, Pathologist’s diagnosis depends on pathology reports of images and from biopsies, provides information about the escalation of cancer through the lymph and other organs of the body. This procedure not only takes a great deal of time and also price. However, it likewise has apparent constraints. The research study shows that the analysis of various pa- thologists has even more significant incongruity. 1 The pri- mary factor for this incongruity is that the pathology medical diagnosis technique is subjective as well as easily affected by the atmosphere. Diagnosis of photos utilizing computer- based algorithms can be an efficient method for sustaining the medical diagnosis. 2 As shown in Figure 1, the bowel is the collection of hollow organs took part in a long, twisting tube starting from duode- num to the anus. It absorbs the fluids and electrolytes, 3 and compels the solid waste to the rectum along with anus for purgation. The abnormal development of cells on the inward lining (mucosa) of the proximal colon or maybe distal colon, known as polyps, may be malignant or benign. Many millions of organs in large intestine and rectum, take care of the absorbing of water as well as minerals and also secreting mucous for the regrowth of epithelial cells. 5 Nevertheless, an age far more than 50, a family his- tory of colorectal cancers, Personal history of uterine, breast cancer, or maybe ovarian cancer can improve the possibility to affect by colorectal cancer. In sporadic cases exposure to carcinogen agents in the environment, specific genetic rea- sons may be the reasons. The stage of disease describes just how much it has spread, the grading stage of cancer helps to choose the best treatment. 6 Figure 2 demonstrates the stages with a number from zero to four. . ABSTRACT Colorectal cancer, which is frequent, recognized tumours in both genders around the globe. As per the report generated by WHO in 2018, colon cancer placed in the third position, whereas 1.80 million individuals are affected. Precisely, it is the succeeding leading cancer, which is the second most common cause of cancer in females, and the third for males. The loss of control over the integrity of epidermal cells in bowel or malignancy can be the cause of colorectal cancer. An effective way to recognize colon cancer at an early stage and substantial treatment can reduce the ensuing death rates to a great extent. To perform Screening of Morphology of Malignant Tumor Cells in the colon, a Gastroenterologist may refer to cancer diagnosis tests for pathological images. In any Histology method, the process takes a significant duration of time due to infinite numbers of glands in the gas- trointestinal system, which may lead to irreconcilable outcomes. By diagnosing through computer algorithms, can give practical and contributory results. Hence, accurate gland segmentation is one crucial prerequisite stage to get reliable and informative morphological image data. In recent times, the scholars applied deep learning algorithms to pathological image analysis for the diagnosis of cancer disease. We propose that features extracted from the diagnostic tests, given as input to a deep learning architecture used along with semantic segmentation algorithm, provide results that are accurate than the existing image seg- mentation algorithms. This work is the extensive review of deep learning architectures used for semantic segmentation on the histological images of the colon. Key Words: Colorectal Cancer, Deep Learning, Gland Semantic Segmentation, SegNet, Histological Images Detection of Colorectal Cancer by Deep Learning: An Extensive Review Mohan Mahanty 1 , Debnath Bhattacharyya 2 , Divya Midhunchakkaravarthy 1 , Tai-hoon Kim 2 1 Department of Computer Science and Multimedia, Lincoln University College, Malaysia; 2 Department of Computer Science and Engi- neering, Koneru Lakshmaiah Education Foundation, Green Field, Vaddeswaram, Guntur 522502, India. IJCRR Section: Healthcare Sci. Journal Impact Factor: 6.1 (2018) ICV: 90.90 (2018) Copyright@IJCRR