In the past few years, the detection of diseases using medical imaging has been a hot area of research, especially in the domain of the gastrointestinal tract. The segmentation of polyps, in particular, has been the major focus because of the availability of ground truths. Furthermore, the classification of gastrointestinal diseases has also been an active area of research. The performance of machine learning algorithms reported in the literature has been quite impressive [17,18], but deep learning algorithms surpass the ML approaches and achieve better results [19].

For the detection of GI tract diseases, numerous studies are available in the literature that use the ML method. For example, in [17], the authors developed a ML model based on the longitudinal training cohort of over 20 thousand patients undergoing treatment for peptic ulcers between the years 2007 and 2016. Their greatest accuracies were 82.6% and 83.3% using logistic regression and ridge regression, respectively. Sen Wang et al. [18] established ML architecture for ulcer diagnosis and performed experimentation on a private developed dataset of WCE videos, 1504 to be exact. The effectiveness of this technique was evaluated using the ROC curve and the AUC, and achieved a 0.9235 peak value. In a different work [13], Jinn-Yi Yeh et al. used color characteristics and a WCE image collection to identify bleeding and ulcers. They used texture information in addition to combining all the picture attributes into a single matrix. Several classifiers, including SVM, neural networks, as well as decision trees, were presented in this matrix of characteristics. Various performance metrics were included for examination, and the accuracy ranged from 92.86% to 93.64%.

It has been observed that deep learning (DL) models generally performed better in detecting GI tract diseases. The authors of [20] developed the VGGNet model based on CNN to detect GI ulcers, with a dataset of 854 images, and achieved 86.6% accuracy. However, these tests took place using conventional endoscopy images. In [21], the authors developed a CNN-based DL model; the dataset consisted of 5360 images containing ulcers and erosions, and contained merely 450 normal class images. The method achieved 90.8% detection accuracy. Sekuboyina and co-authors, in [22], proposed models based on CNN to detect dissimilar forms of diseases in WCE images, like ulcers, and more. They developed multiple subsections of images and applied the DL model. This experiment attained 71% sensitivity and 72% specificity.

Apart from the classification techniques, researchers also proposed segmentation techniques for the detection of the predecessor disease of colorectal cancer. A fully convolutional network (FCN) was proposed in [23], which is trained from start to finish as well as pixel by pixel, and yields the segmentation of polyps. There are no extra postprocessing procedures needed for the suggested model, which is the major contribution of this research. In another paper [24], the authors discussed and enhanced the FCN network and named it the U-Net architecture. The U-Net model achieved good results for localization. Furthermore, many researchers have tried to modify and enhance the U-Net architecture to achieve better segmentation and localization results [25,26,27], but in medical images, these are not evaluated or do not provide better results. By maximizing the characteristics gleaned from two pre-trained models, the authors of [28] established a framework for gastrointestinal illness categorization and achieved 96.43% accuracy. In another framework [29], MobileNet-V2 is used for the multiclass classification of gastrointestinal illnesses, and a contrast enhancement approach was suggested.

Based on the literature, it can be said that sufficient related work has been performed in the field of GI tract disease detection and classification. The presented results show reasonable performance in terms of accuracy. However, performance can be improved. Accuracy is an important performance metric; however, for multiclass classification problems, accuracy is less significant as compared to other performance metrics, especially when there is an imbalance in the dataset. For instance, we would like to emphasize that precision and recall rate are important performance measures for life-critical applications. Most of the presented works have reasonable accuracy, but they suffer from lower precision and recall rate, and require improvement.