A Systematic Review of Deep Learning Techniques in Colon Cancer Screening

Deep Learning is nowadays one of the powerful approaches of the Artificial Intelligence field in object detection and recognition. It also has gained reputation for improvement in the analysis of medical images. Early diagnosis and accuracy in prediction can be obtained by finding various normal and abnormal patterns in complex datasets by using different deep learning algorithms. Once deep learning models have undergone extensive training on enormous datasets, it can learn distinguishing between tissues that are either cancerous or not. Moreover, it improves precision in diagnosis and also enables better results in the outcomes of patients. This paper elaborates various deep learning architectures employed in investigating the state of the colon in diagnosing cancer. Deep learning algorithms which detect colon cancer help to identify various significant performance parameters to measure their effectiveness. The metrics include accuracy which helps to make the correct predictions, sensitivity metric to correctly identify the patients suffering from the disease and specificity metric to identify the people without the disease accurately. This paper will present reviews of different research papers that outline the overall framework. This paper will be beneficial for those researchers who are keen to know about different deep learning techniques for diagnosing the cancer in colon.