# Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review
## 3 Deep Learning-Based Studies for CRC Diagnosis
### Polyp Detection
Another similar study [71] applied state-of-the-art DL algorithms to each colonoscopy frame of a gastrointestinal image analysis (GIANA) (dataset available at [[https://giana.grand-challenge.org/](https://giana.grand-challenge.org/)]([https://giana.grand-challenge.org/](https://giana.grand-challenge.org/)) (accessed on 23 July 2021)) that consisted of 18 videos collected from endoscopic results of multiple patients. For data preprocessing, the black edges of the endoscopy image frames were removed, and the images were resized to model-specific dimensions of 284 \(\times\) 265 pixels before being passed for data augmentation with horizontal and vertical flips and a blur filter. This approach used ResNet-50 as a fully convolutional neural network (FCNN) to extract descriptive characteristics from the input image. The extracted features were then subjected to a faster RCNN [72] model with two FCLs, each operating as a regression and a classification layer. After extensive experiments and evaluations, their model achieved a precision value of 80.31%, recall of 75.37%, accuracy of 71.99%, and specificity of 65.70%. Different levels of diagnostic accuracy can be observed by adapting different strategies for data preprocessing and by using DL algorithms. The optimal solution for medical image diagnostics is, however, not obtained through minimal trials and testing, but is considered via continuous and long-running research. In this context, several studies tried to overcome the shortcomings in previous research or developed a completely novel scheme for CRC diagnosis. Considering this, to enhance detection accuracy obtained by reference studies, [PERSON] et al. [73] developed and validated a robust DL algorithm for use in the detection of colorectal polyps. In that study, the authors collected endoscopy data samples from the Asan Medical Center, Korea, between May 2017 and February 2018. The whole training dataset contained 8075 images from 185 colonoscopy videos of 103 patients. Figure 5: Polyp image samples. (**a**) original image, (**b**) corresponding annotation, and (**c**) detected polyps in the yellow regions. For validation and testing, different sets of data samples were collected from the same place. These datasets were preprocessed by storing them at a fixed resolution of 475 \(\times\) 420 pixels before labeling the location and dimension of each polyp in the image with bounding boxes. This study used a one-shot classification model of every object present in the image using YOLO v2 [74] without using an attention mechanism. The classification model was a fine-tuned Darknet19 model provided at [[https://pjreddie.com/darknet/](https://pjreddie.com/darknet/)]([https://pjreddie.com/darknet/](https://pjreddie.com/darknet/)) (accessed on 26 July 2021), which was pretrained on an ImageNet dataset. By creating B bounding boxes with a confidence score for the class probability of each box, the model was able to secure a sensitivity level of 96.7% with a false positive rate (FPR) of 6.3%. Similarly, in 2020, [PERSON] et al. [75] developed a classification model for use in the identification of adenomas, [PERSON]'s disease, ulcerative colitis, and normal images by using endoscopic image samples from CRC patients. They used two datasets: the first was provided by Gill Hospital in Korea with a total of 3515 images, and the second was a publicly available KVASIR dataset [19] with 4000 endoscopy samples. Each dataset was normalized to model-specific inputs and was subjected to augmentation, which included flipping, scaling, rotating, zoom, contrast normalization, and shearing. A transfer learning approach was used with a ResNet-50 architecture as a baseline model, initialized with pretrained weights from ImageNet. An efficient dilation technique [76] was adopted to preserve the spatial information of the final layers in the network by using dilated convolution layers in ascending and descending order.