# Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review
## 3 Deep Learning-Based Studies for CRC Diagnosis
### MSI Detection  
Microsatellites, which are also known as short tandem repeats (STRs), are tiny repeating stretches of DNA that are scattered across the entire genome region, accounting for approximately 3% of the whole region [55]. The MSI phenotype is one of the molecular changes that occurs in CRC, and it is also observed in different types of cancer, such as adrenocortical, rectal, colon, stomach, and endometrial tumors, and breast and prostate cancer [56]. MSI can also be referred to as a hyper-mutable phenotype that is an outcome of deficient mismatch repair (dMMR). In Figure 4, we can see MSI patches, indicated by yellow arrows, that show activations around the potential patterns of infiltrating immune cells. The identification of MSI status in CRC patients is crucial, because it helps to determine the presence of related diseases such as lynch syndrome, a highly penetrant hereditary cancer syndrome accounting for one-third of patients with MSI. Therefore, a less labor-intensive and broadly accessible MSI testing tool based on DL approaches has been studied lately. These CAD systems contribute an automated screening tool to triage patients when making clinical decisions, so as to identify differential treatment responses. The authors in [57] introduced adversarial MSI-based assessment (AMIBA), a modality to diagnose microsatellite instability directly from histopathological images. Histological image data with a clinically determined MSI status (MSI-H, MSI-L, and MSS) were obtained from TCGA available at [[https://portal.gdc.cancer.gov/](https://portal.gdc.cancer.gov/)]([https://portal.gdc.cancer.gov/](https://portal.gdc.cancer.gov/)) (accessed on 21 July 2021), where high (H), low (L), and microsatellite stable (MSS) each represents highest, lowest, and no presence of microsatellites. For data preprocessing, the image slides were clipped into non-overlapping image patches of 1000 \(\times\) 1000 pixels, which were obtained at 20 \(\times\) magnification from the original slide. Furthermore, patches with more than half of the area empty were not included in the process, which generated a total of 620,833 patches to train the specific DL architecture. Multiple state-of-the-art DL architectures were used,  
Figure 4: MSI cells in H&E-stained image patches (indicated by yellow arrows). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Publication (Year)** & **Objective** & **Method Used** & **Image Types** & **Optimum Results** \\ \hline \multirow{6}{*}{[54] (2021)} & \multirow{4}{*}{To develop a CNN-based CAD system for predicting the pathological histology of colorectal adenomas} & **Data preprocessing**: Standardized input images excluded unnecessary black areas by cropping them to 480 \(\times\) 480 pixels. **Model: Multiple CNN models (ResNet-50 [43], Inception-v3 [48], and DenseNet-161 [49]) with a new classification head consisting of a single FCL. & \multirow{4}{*}{Endoscopic images} & \multirow{4}{*}{Specificity = 92.42\% PPV = 77.16\% NPV NPV = 92.58\% NPV = 92.  
including ResNet-18 [43], AlexNet [40], and VGG-19 [34], where weights were initialized with parameters pretrained on an ImageNet dataset [58]. By using the Adam optimization algorithm with a learning rate of 0.0001, the authors obtained patch-level and slide-level accuracies of 91.7% and 98.3%, respectively. Considering ways to facilitate universal MSI screening, the research in [59] studied how deep residual learning can predict the MSI status directly from H&E-stained histological slides. Multiple datasets regarding MSI status were collected from large patient cohorts in TCGA, which were manually annotated and classified to represent one tumor tissue and two non-tumor tissues (dense and loose tissue). The image slides were preprocessed to create 11,977 unique image tiles, each with a 256 \(\upmu\)m edge length. Furthermore, to convert all the images into a reference color, a color normalization technique based on the Macenko method was used.