# Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review
## 3 Deep Learning-Based Studies for CRC Diagnosis
### Tumor Tissue Detection and Classification
In this study, the original H&E-stained WSIs were split into uniform-dimension tiles of 224 \(\times\) 224 pixels, after which, VGG16 [34] (a popular pretrained CNN model) was used to extract intermediate features from the image patches. The extracted 4096 bin feature vector was classified into several tumor types by using a combination of long short-term memory (LSTM) [35] with one of three classifiers: a support vector machine (SVM), logistic regression, or naive Bayes. LSTM is a type of recurrent neural network (RNN) that is well suited for classifying, processing, and making predictions on the time series data and is famous for its capability of learning the long-term temporal dependencies of input data. The model's performance was assessed with different accuracy metrics, where an area under the curve (AUC) value of 0.69, a hazard ratio of 2.3, and a 95% confidence interval (CI) were achieved. Similar to the previous study, [PERSON] et al. [36] also used a well-known VGG16 framework with some notable changes to its architecture, where classification was carried out with a voting classifier and an SVM classifier. In this study, data preprocessing was applied to the H&E slides before passing them on for feature extraction. The steps included chromatic normalization of the image patches at 224 \(\times\) 224 pixels and data augmentation to increase the number of samples for better generalization of the network. The patch level accuracy and F1-score were found to be 70% and 0.67, respectively, while a cluster level experiment significantly outperformed the former with a staggering accuracy of 100% and a unit F1-score. In DL, the accuracy of a model is significantly dependent upon the type of feature extractor and the classification procedures [37]. Therefore, multiple studies considered using a variety of popular CNN models or designed a model from scratch with the optimal tuning of hyper-parameters. To make use of multiple pretrained models and to evaluate their performance, [PERSON] et al. [38] investigated whether the existing pretrained CNN  
Figure 3: Sample images from a CRC dataset: (**top row**), endoscopy images, and (**bottom row**), histological whole slide images (WSI). models could extract the prognosticators directly from H&E-stained tissue slides. Human cancer tissue slides from multiple patient cohorts (NCT biobank at [[http://dx.doi.org/10.5281/zenodo.1214456](http://dx.doi.org/10.5281/zenodo.1214456)]([http://dx.doi.org/10.5281/zenodo.1214456](http://dx.doi.org/10.5281/zenodo.1214456)) (accessed on 18 July 2021), a DACHS study at [[http://dx.doi.org/10.5281/zenodo.1214456](http://dx.doi.org/10.5281/zenodo.1214456)]([http://dx.doi.org/10.5281/zenodo.1214456](http://dx.doi.org/10.5281/zenodo.1214456)) (accessed on 18 July 2021), and a TCGA cohort at [[http://cancer.digitalslidearchive.net](http://cancer.digitalslidearchive.net)]([http://cancer.digitalslidearchive.net](http://cancer.digitalslidearchive.net)) (accessed on 18 July 2021) (NCT: National Center for Tumor Diseases, DACHS: Darmkrebs Chancen der Verhutung durch Screening, and TCGA: The Cancer Genome Atlas) were used as training and testing datasets. For data preprocessing, they created several non-overlapping image patches, each at 224 \(\times\) 224 pixels, and normalized them with the Macenko method [39]. Five pretrained models (VGG19 [34], AlexNet [40], SqueezeNet v1.1 [41], GoogLeNet [42], and ResNet-50 [43]) were used for feature extraction, while classification was carried out by replacing the classification head with a new fully connected layer. Among them, the best classification accuracy was achieved by VGG19, which was trained on a full set of 100,000 images and tested with an external test set of more than 7000 images, while the least accurate model was SqueezeNet, with a classification accuracy of less than 50%.