# Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review
## 1 Introduction  
Global cancer statistics from 2018 show that the incidence of colorectal cancer (CRC) ranks highest after lung cancer and breast cancer, and worldwide, it accounts for approximately 10% of the total annual cancer patients among both men and women [1]. Although people aged 65 years and above are the most prevalent victims of this disease, the risk in younger patients is also significant, with the highest risk due to heredity (35%) followed by other factors such as obesity, bad nutritional habits, and smoking [2]. These rates show no trend toward decline, but rather are expected to increase by more than 60% in the next decade, with more than two million new diagnoses and over a million deaths by the next decade [3]. In this regard, there is a need to develop an optimal diagnosis strategy for the early and precise detection of CRC patients. With routine screening being an important step for the reduction in mortality rates of this disease, colonoscopy (an endoscopic method) is considered a primary and straightforward clinical diagnosis method of choice for CRC [4]. Aside from this method, medical imaging techniques such as CT colonography, a complementary imaging method for polyp detection in CRC, and the histological evaluation of hematoxylin and eosin (H&E) slides remain indispensable approaches to subtle inspections for CRC. While manual observations of these imaging modalities by individual pathologists have been pursued relentlessly, recently, they have been modeled as traditional and unsophisticated approaches that are highly labor-intensive and time-consuming. Besides, inter-observer variation can be significant during pathological diagnosis, resulting in the biased analysis of typing and gradingof cancer tumors [5]. Therefore, a more standardized and automated technique based on computer-aided diagnosis (CAD) has gained a lot of interest and demand lately. Many CAD systems have been utilized in mainstream radiology to assist physicians, from improved chest X-rays and mammography applications in the 1960s to enabling the early diagnosis of cancers in the 2000s. Considering the medical and economic burdens caused by the prognosis and treatment of CRC-related diseases, researchers have been focusing on developing CAD systems for use in the early and effective diagnosis of CRC. The development of CAD systems for CRC can be dated from the conventional models that require complex a priori knowledge of mathematics [6; 7; 8] to advanced machine learning (ML)-based systems [9; 10; 11; 12] that can perform beyond human levels of accuracy. Although cancer diagnosis with deep learning (DL) has been a very popular subject of interest in the medical imaging domain, comprehensive literature reviews covering various aspects of CRC diagnosis and prognosis using state-of-the art DL schemes are still limited. The existing studies lack surveys based on various types of available standard CRC imaging datasets. In addition, in a short period of time, there has been adequate novel research and findings from DL-based CRC diagnoses. A proper review of these state-of-the-art findings in terms of adapted data preprocessing strategies is required, and a methodology needs to be developed to facilitate upcoming researchers and scholars in this field. Therefore, this review paper intends to fill this gap in four ways. First, it provides a brief retrospective overview of conventional CAD systems based on simple image processing and ML-based approaches to CRC diagnosis. Secondly, we identify and list some of the publicly available imaging datasets collected and archived from various independent sources, which are standardized for DL-based CRC diagnosis. Thirdly, we systematically categorize and highlight the latest studies on DL-based detection, diagnosis, and prognosis regarding different types of CRC, including tumors, microsatellite instability (MSI), and polyps.