# Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review
## 3 Deep Learning-Based Studies for CRC Diagnosis
### Polyp Detection  
The beginning phase of most CRCs is stimulated with a growth of tissues on the inner lining of the colon. These abnormal growths of tissue from the mucous membrane, developing over a period, are called polyps, and are often considered a precursor to CRC. Figure 5a-c, respectively, show a polyp image from a DL-based CAD system extracted from a CRC patient, the corresponding annotations, and the detected polyps. Colonic polyps, especially with a large size and in large numbers, are more likely to be cancerous, and if not treated early, they could develop into colon cancer. CRC polyps can be categorized as neoplastic and non-neoplastic. The former is non-cancerous, while the latter can develop into cancer and can be further sub-categorized into adenomas and serrated polyps. In clinical practice, the detection of polyps is usually accomplished via colonoscopy, which is an expensive, manual, and time-consuming procedure. Frequent reviews of colonoscopy data are required, because 20% of polyps are likely to be missed during a single review. This is extremely labor intensive, and the lack of a thorough inspection of the data might result in the missed detection of polyps. Taking this into account, an automated and non-invasive  
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**Publication (Year)** & **Objective** & **Method Used** & **Image Types** & **Optimum Results** \\ \hline \multirow{9}{*}{[65] (2020)} & \multirow{3}{*}{To predict RNA-Seq profiles from WSI without annotation and to identifying tumors containing MSI containing MSI} & \multicolumn{3}{c}{**Data preprocessing**: Divided WSI into squares tiles of (224 × 224 pixels); segmented thresholding **Model**: Multi-layer perceptron applied to every tile with 5-fold cross-validation.} & \multirow{3}{*}{Histology images} & \multirow{3}{*}{AUC-ROC = 0.83} \\  & & & & \\ \cline{1-1} \cline{5-5}  & &  
procedure based on CAD has been reliable and is considered more robust for accurate detection of polyps. Specifically, DL-based segmentation and classification algorithms have been indispensably applied recently to enable the routine detection of polyps in CRC diagnosis. In this regard, in [68], multiple studies related to colon cancer analysis were collected under the field of colon cancer and deep learning; then, they were categorized into five categories that are detection, classification, segmentation, survival prediction, and inflammatory bowel diseases. In [69], the current systematic review on colorectal cancer detection and localization, and difficulties of a fair comparison and the reproducibility of those methods were addressed. A conference paper was published by [PERSON] et al. [70] in 2017 with the objective being the automatic detection of polyps via CT colonography using DL techniques. To achieve this, a CT colonography image dataset was collected from the Cancer Imaging Archive (TCIA), available at [[https://www.cancerimagingarchive.net/](https://www.cancerimagingarchive.net/)]([https://www.cancerimagingarchive.net/](https://www.cancerimagingarchive.net/)) (accessed on 23 July 2021), containing ground truth information for segmentation purposes. In the data preprocessing steps, the authors discarded the air gap-filled regions in the colon images by using thresholding and filtering techniques. Furthermore, to label each block in the image, binary region of interest (ROI) segmentation was performed. A CNN model with three convolutional layers, three max pooling layers, and a single FCL was designed and trained using 10-fold cross-validation, which obtained classification accuracy, sensitivity, and specificity levels of 88.56%, 88.77%, and 87.35%, respectively.