# Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review
## 4 Discussion  
Multiple DL algorithms discussed in aforementioned sections have achieved highly reliable results in accurately detecting different types of tumors, MSI cells, and colorectal polyps. The evaluations of these models have been conducted through several validation tests and are designed to perform domain specific tasks such as segmenting tumorous from non-tumorous tissue or classifying cancerous cells from the healthy ones. For tumor tissue classification tasks, the EfficientNet [88] model was shown to display superior performance, while U-Net [90], and YOLO architectures [74; 79; 84] showed high precision in solving polyp segmentation and detection tasks, respectively. Using DL methods, the clinical inspection of CRC-related patients is quickly performed with high diagnostic accuracy. With that being said, the accuracy of DL algorithms can vary significantly and is dependent upon the amount of data with which the model is trained. Especially in the medical imaging sector, the availability of a publicly usable, large-scale standard dataset for \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Publication (Year)** & **Objective** & **Method Used** & **Image Types** & **Optimum Results** \\ \hline \multicolumn{6}{c}{**Data preprocessing: Normalized images by subtracting mean of the image by its standard deviation; annotated with bounding boxes; resized to fixed 512 \(\times\) 512 pixels; applied data augmentation such as horizontal, vertical flipping, random rotation, random scale, and random cropping.** **Model: Used multiple models: feature extraction using EfficientDet [87] (EfficientNet [88] as a backbone architecture) with a bidirectional feature pyramid network (FPN), and a shared class/box prediction network; Faster R-CNN as detector network with region proposal network (RPN) as the proposal network, YOLOv3 [79] with multi-class logistic loss modeled with regularizers such as objectness prediction scores, and YOLOv4 [84] with on-the-fly data augmentation such as mosaic, and cut-mix.** & **Model: Used multiple models: feature extraction using EfficientDet [87] (EfficientNet [88] as a backbone architecture) with a bidirectional feature pyramid network (FPN), and a shared class/box prediction network; Faster R-CNN as detector network with region proposal network (RPN) as the proposal network, YOLOv3 [79] with multi-class logistic loss modeled with regularizers such as objectness prediction scores, and YOLOv4 [84] with on-the-fly data augmentation such as mosaic, and cut-mix.** & **Endoscopy images** & **Accuracy = 96.0\% images** \\ \hline \multicolumn{6}{c}{**Data preprocessing: Removed unclean and unclean colorectal images with data filtering; labeled images into one of three classes: normal tissue, polyp, and tumor; split the dataset into train, validation, and testing set with the ration of 2:1:2; applied data augmentation; resized to 440 \(\times\) 440 \(\times\) 3 to maintain dimensional uniformity. Model: Used self-paced regularization method to assign different sample weights for different training samples; VGG19 pretrained on ImageNet dataset for feature extraction; replaced FCL with GAP layer for classification of colorectal images; used U-Net-based [90] automatic polyp-region segmentation.** \\ \hline \hline \end{tabular}
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Table 5: Some recently published articles on polyp detection in CRC by using DL techniques. conducting experiments is considered rare, relative to other fields, for example, in natural images (ImageNet). In most scenarios, to complement the scarcity of vast amounts of data, techniques such as data augmentation are widely practiced. Ranging from traditional augmentation techniques such as flipping, shifting, and rotation, novel techniques such as generative adversarial networks (GANs) [91] and style transfer techniques [92] have also been extensively used to create and add synthetic instances to increase the data samples, guaranteeing higher efficiency in the DL models.