# Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review
## 3 Deep Learning-Based Studies for CRC Diagnosis
### Tumor Tissue Detection and Classification  
Tumors are complex structures in a human organ that comprise multiple and distinct types of tissue. They can be interpreted as abnormal tissue composed of multiple types of cells or a matrix of cells. In CRC, the architecture of a tumor is varied, along with its development, and is the major factor in patient prognosis [25]. Therefore, an automated and highly quantitative analysis of tumor tissue is indispensable for the clinical diagnosis of CRC. Automatic analysis of these tissue regions can be helpful in quantifying their extent, in the grading of tumors, and to investigate a biological hypothesis based on tissue morphology. Figure 3 shows the different types of tumor tissues obtained from H&E-stained histological slides that are relevant to CRC. These tissue types, when evaluated by pathologists, are visually classified into one of eight different categories (tumor, stroma, complex, lympho, debris, mucosa, adipose, and empty). A DL-based CAD system can automatically classify these tumor regions if provided with adequate amounts of data and if trained with optimal network hyper-parameters. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Name** & **Release Date** & \
\begin{tabular}{c} **Total Number of Image** \\ **Samples** \\ \end{tabular} &
\begin{tabular}{c} **Average Dimensions** \\ **(in Pixels)** \\ \end{tabular} \\ \hline \multirow{5}{*}{Endoscopy images} & The KVASIR dataset [19] & 2017 & 1000 & 720 \(\times\) 576 to 1920 \(\times\) 1072 \\ \cline{2-4}  & The Neverthus dataset [20] & 2017 & 5525 & 750 \(\times\) 576 \\ \cline{2-4}  & The Hyper-Kvasir dataset [21] & 2019 & 110,079 & 224 \(\times\) 224 \\ \cline{2-4}  & CVC-Colon DB [22] & 2017 & 356 & 500 \(\times\) 574 to 1080 \(\times\) 1920 \\ \cline{2-4}  & CVC-Clinic DB [23] & 2012 & 612 images & 384 \(\times\) 288 \\ \cline{2-4}  & Endoscopy disease detection (EDD) and segmentation [24] & 2020 & 386 images & 400 \(\times\) 400 \\ \hline \multirow{5}{*}{Histology images} & Kather texture dataset [25] & 2016 & 5000 patches & 150 \(\times\) 150 \\ \cline{2-4}  & Colorectal adenocarcinoma gland (CRAG) [26] & 2019 & 38 WSI & 1512 \(\times\) 1516 \\ \cline{2-4}  & Colorectal nuclear segmentation and phenotypes (CoNSeP) [27] & 2018 & 41 WSI & 1000 \(\times\) 1000 \\ \cline{2-4}  & CRC-TIA [28] & 2017 & 139 WSI & 1792 \(\times\) 1792 \\ \cline{2-4}  & Histological images for tumor  detection in gastrointestinal cancer [28] & 2019 & 11,977 patches & 512 \(\times\) 512 \\ \cline{2-4}  & Pathology AI platform (PAIP) [29] & 2019 & 118 WSI & 29,879 \(\times\) 23,066 \\ \cline{2-4}  & Warwick-QU dataset [30] & 2016 & 166 images & 775 \(\times\) 522 \\ \hline UniToPatho [31] & 2021 & 9536 patches & 224 \(\times\) 224 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Some of the publicly available CRC imaging datasets. Multiple studies based on DL have been conducted to this end in order to accurately classify CRC tumor regions. [PERSON] et al. [32] proposed a CNN framework to distinctly classify adenocarcinomas (a type of tumor) from healthy tissues and benign lesions. As a preprocessing step, they created a total of 13,500 image patches with dimensions of 1089 \(\times\) 1096 at 40\(\times\) magnification of the original whole slide images (WSIs) from H&E-stained images. Subsequently, to compensate for the color inconsistencies, sets of whole image patches were normalized based on mean and standard deviation. These preprocessed datasets were fed into a CNN model with thirteen convolutional layers, five max pooling layers, and three fully connected layers (FCLs) to univocally classify them into one of three tissue subtypes, namely adenocarcinoma, tubulovillous adenoma, and healthy tissue. An initial classification accuracy of around 90% was obtained, which was optimized to 96% by using a transfer learning strategy. Another study [33] developed DL-based automated analysis of CRC image samples with the objective of improving the prognostic stratification of patients.