# Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review
## 3 Deep Learning-Based Studies for CRC Diagnosis
### MSI Detection
The authors conducted initial experiments with multiple convolutional layers, from which ResNet-18 was selected as the optimum model due to its noteworthy advantages, such as a short training time, better classification performance, less risk of overfitting, and comparatively fewer training parameters. All models were trained on an ImageNet dataset, and only the weights of the last 10 layers were fine-tuned, while the rest of them were frozen. By using the Adam optimizer [60] and L2 regularization with multiple learning rates [\(10^{-5}\), \(10^{-6}\)], they obtained an area under the curve (AUC) of 0.99. The CI for both true MSI and MSS tiles was found to be 95%. Another study conducted in 2020 by [PERSON] et al. [61] developed a two-stage DL-based classification pipeline for predicting MSI status in CRC patients. In the two-stage process, the first stage was responsible for segmenting the tumor area into two types of tissue (MSI-H and MSI-L). The latter stage then classified the tissue types into their corresponding class. H&E-stained histological WSIs annotated by professional pathologists were obtained from a pathology AI platform (PAIP) at [[http://wisepaip.org/paip](http://wisepaip.org/paip)]([http://wisepaip.org/paip](http://wisepaip.org/paip)) (accessed on 21 July 2021) and were preprocessed before being used as input to the DL architecture. During preprocessing, the WSIs were cropped to magnifications of 20 \(\times\) and 10 \(\times\) to obtain image patches of 224 \(\times\) 224 pixels before converting the RGB images to the CIE L \(\times\) a \(\times\) b color space. Other preprocessing methods, such as foreground mask extraction with Otsu's thresholding, were used to segment individual patches. Two DL models were adapted in this research: the feature pyramid network (FPN) [62] and inception ResNet-V2 [43], one for each stage in the classification pipeline. Multiple optimization algorithms such as Adam and RMS prop were used, with each being trained on one of two learning rate schedulers (step decay and cosine annealing) with a learning rate of 10-4. The optimum precision, recall, and F1-score were found to be 0.93, 0.93, and 0.94, respectively. Similarly, to develop a DL system for detecting CRC tumor specimens with MSI, [PERSON] et al. [63] collected H&E-stained slides from 8836 CRC tumors from the MSI-DETECT consortium ([[https://jnakather.github.io/msidetect/](https://jnakather.github.io/msidetect/)]([https://jnakather.github.io/msidetect/](https://jnakather.github.io/msidetect/)) (accessed on 22 July 2021)). All specimens belonged to a large cohort of patients from Germany, the Netherlands, the United Kingdom, and the United States, where each specimen with MSI was identified via genetic analysis. These data were preprocessed by tessellating the slides into individual square tiles of 256 \(\upmu\)m edge lengths followed by color normalization with the Macenko method before passing them to a ShuffleNet model [64] for classification. The whole model was trained on Nvidia RTX6000 graphical processing unit (GPU) hardware with the Adam optimizer, L2 regularization, and a learning rate of 5 \(\times\) 10\({}^{-5}\). The classification results were evaluated based on several performance metrics, where the optimal values of area under the receiver operating characteristics (AUROC), area under the precision recall curve (AUPRC), sensitivity, and specificity were recorded as 0.96, 0.9, 99%, and 98%, respectively. Recently, several other DL-based studies for MSI detection and/or classification were conducted, which are listed in Table 4.