The use of medical imaging to identify diseases has gained popularity in recent years, particularly in the field of the gastrointestinal system. Another active field of research is the classification of digestive illnesses. Although machine learning algorithms have demonstrated amazing performance in the literature [15,16], CNN algorithms outperform ML techniques and produce superior results [17].

Several research [18,19,20,21,22,23,24] works have examined the detection of abnormal frames in capsule endoscopy images, including the detection of tumors, Crohn’s disease, polyp, hemorrhages, ulcers, lymphangiectasia, and other intestinal lesions. Existing techniques often start with feature extraction and then apply a detection technique. The detection techniques either use multi-label approaches to find and classify various types of abnormality [19] or distinguish between frames with a lesion and normal ones [18]. These methods frequently extract features from the images’ texture and morphological analysis, statistical feature analysis, and color descriptors. These techniques either use region-based [20,21] or pixel-based [18,19] methodologies. Artificial neural networks (ANNs) and support vector machines (SVMs) are the two commonly utilized classification techniques in the literature [22,23,24]. Many other studies related to gastrointestinal diseases such as polyp, colon, and capsule endoscopy have been conducted in the literature [25,26,27,28,29,30].

DL models such as ResNet-50, VGG16, and Inception-V3 were used by Lee et al. [31] to categorize normal and ulcer GI pictures. Resnet-50 outperformed the other deep networks using this technique. A saliency-based strategy was presented by Khan et al. [32] to segment GI diseases, whereas DL architecture is employed for classification. They combined a YIQ color space with an HSI color space, which was then fed into a segmentation method based on contours. An automated technique for identifying an ulcer from the WCE frames was introduced by Yuan et al. [33]. To begin with, a saliency technique based on superpixels is used to define the boundaries of the ulcer zone. The level-by-level texture and color properties are then computed and combined to produce the final saliency map. Then, to achieve a recognition rate of 92.65%, the saliency max-pooling (SMP) and locality-constrained linear coding (LLC) techniques are combined. With a dataset of 854 photos, the authors of [34] proposed the VGGNet model, which was built on a CNN (convolutional neural network), to accurately identify gastrointestinal ulcers, even though these tests used images from a standard endoscopy. The dataset used in [35] comprised 5360 WCE images with ulcers and erosions and only 440 normal images; the authors created a model based on a CNN. This method’s detection accuracy was 90.8%. An attention-based DL architecture for classifying and localizing stomach diseases from WCE images was presented by Jain et al. [36]. They started by effectively classifying stomach illnesses using CNN. Later, for the localization of contaminated areas, they combined Grad-CAM++ and a unique SegNet. On a KID dataset, the proposed technique was tested, and it showed enhanced accuracy. For WCE video summarization, Lan et al. [37] developed a combination of unsupervised DL techniques. They employed several networks, including LSTM and autoencoder, among others. This research’s major goal was to assist medical professionals in their examination of the complete WCE videos. In [38], a gastrointestinal disease classification framework is proposed in which deep features are selected and fused from two deep models, i.e., ResNet-50 and ResNet-152. Subsequently, the features were optimized and it achieved 96.43% classification performance. In another work, [25], alimentary diseases such as Barret, Polyp, and Esophagitis were classified by applying discrete wavelet transform and CNN. This framework achieved a 96.65% accuracy on the Hyper Kvasir dataset.