# Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review
## 3 Deep Learning-Based Studies for CRC Diagnosis
### Datasets  
Data constitute an inevitable part of a DL algorithm, from which the model can learn concealed information or underlying statistics available within them. Data can be in any form such as numbers, audio, images, and videos. Dataset preparation is a long process that includes collection, analysis and treatment, exploration, training, and testing. The data from which the model is trained must be relevant to the specific problem and must resemble real-world data as much as possible. To train a DL model, a large amount of data with significant standard deviation is required. With more data, better accuracy from a DL algorithm can be acquired as the model learns an abundant number of variations and recognizes invariant features and discrete instances of the input samples.  
In medical imaging, data are acquired for several purposes, including but not limited to disease diagnosis, therapy planning, intraoperative navigation, and biomedical research [17]. Unlike normal imaging modalities, medical image data are hard to acquire  
Figure 1: Block diagram showing the fundamental difference between working mechanisms of ML and DL algorithms for classifying images as cancer or non-cancer subtypes.  
due to privacy and confidentiality considerations. Besides that, requirements for accurate imaging, specific contrast, minimal artifacts, and a sufficient signal-to-noise ratio make it hard to obtain the optimum image quality required for clinical practice. In cancer diagnosis, particularly in the CRC domain, the analysis of endoscopy/colonoscopy image samples, as shown in Figure 2 (top row), has been popular in the past. Capturing colonoscopy images is considered an invasive procedure where a tiny tube is inserted along the entire length of the colon to provide an interior view of cross-sectional areas. Histopathology imaging, as shown in Figure 2 (bottom row), on the other hand, is a less invasive procedure that provides a more comprehensive view of the disease, and it preserves the underlying tissue architecture. Due to a lack of computational resources and the high cost of digital imaging equipment, this image modality has been overlooked in the past. However, thanks to the high-end computational resources recently developed, spatial analysis of histopathology imagery has been considered the backbone of most automated image analysis techniques and remains the undisputed best way to diagnose vast numbers of diseases, including all cancer types [18]. In digital pathology, histological images are stained with H&E to view cellular and tissue structural details. These H&E-stained slides are utilized to confirm the presence or absence of disease, for disease grading, and for measuring disease progression in CRC.  
To this end, different types of CRC datasets belonging to either colonoscopy or histological imaging have been introduced. These images are preprocessed by applying several techniques before passing them to DL algorithms for specific tasks, such as detection, segmentation, and classification. Table 2 lists some of the popular datasets used in multiple studies based on developing DL-based CAD systems. These datasets provide comprehensive imagery of CRC tissue and tumors and entail disease-specific characteristics that are annotated by experienced pathologists.  
Figure 2: Sample images from a CRC dataset: (**top row**), endoscopy images, and (**bottom row**), histological whole slide images (WSI).