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Wireless Capsule Endoscopy (WCE) has fundamentally transformed diagnostic methodologies for small-bowel (SB) abnormalities, providing a comprehensive and non-invasive gastrointestinal assessment in contrast to conventional endoscopic procedures. The King Abdulaziz University Hospital Capsule (KAUHC) dataset comprises annotated WCE images specifically curated for Saudi Arabian residents. Comprising 10.7 million frames derived from 157 studies, KAUHC has been classified into Normal, Arteriovenous Malformations, and Ulcer categories. Following the application of specific inclusion and exclusion criteria, 3301 labeled frames derived from WCE 86 studies were identified. Upon admission of patients, the data collection phase of KAUHC was initiated, involving the administration of the OMOM capsule and the use of the OMOM recording device for video documentation. A thorough evaluation of these recordings was undertaken by multiple gastroenterologists to identify any pathological abnormalities. The identified observations are subsequently extracted, categorized, and prepared for validation using Machine Learning (ML) classifiers. The dataset aims not only to address the scarcity of annotated endoscopic imaging resources in the Middle East but also to advance the development of diagnostic tools for ML applications in SB abnormalities and exploratory research on gastrointestinal diseases.

Semantic segmentation of Remote Sensing (RS) images involves the classification of each pixel in a satellite image into distinct and non-overlapping regions or segments. This task is crucial in various domains, including land cover classification, autonomous driving, and scene understanding. While deep learning has shown promising results, there is limited research that specifically addresses the challenge of processing fine details in RS images while also considering the high computational demands. To tackle this issue, we propose a novel approach that combines convolutional and transformer architectures. Our design incorporates convolutional layers with a low receptive field to generate fine-grained feature maps for small objects in very high-resolution images. On the other hand, transformer blocks are utilized to capture contextual information from the input. By leveraging convolution and self-attention in this manner, we reduce the need for extensive downsampling and enable the network to work with full-resolution features, which is particularly beneficial for handling small objects. Additionally, our approach eliminates the requirement for vast datasets, which is often necessary for purely transformer-based networks. In our experimental results, we demonstrate the effectiveness of our method in generating local and contextual features using convolutional and transformer layers, respectively. Our approach achieves a mean dice score of 80.41%, outperforming other well-known techniques such as UNet, Fully-Connected Network (FCN), Pyramid Scene Parsing Network (PSP Net), and the recent Convolutional vision Transformer (CvT) model, which achieved mean dice scores of 78.57%, 74.57%, 73.45%, and 62.97% respectively, under the same training conditions and using the same training dataset.