# Abnormalities and Disease Detection in Gastro-Intestinal Tract Images Source: [Abnormalities and Disease Detection in Gastro-Intestinal Tract Images]() Collection index: [llms.txt]() Data updated: 2026-03-26T05:00:04.122Z Authors: Zeshan Khan, Muhammad Atif Tahir Published: 2026-03-23T10:13:56.000Z ## Abstract Gastrointestinal (GI) tract image analysis plays a crucial role in medical diagnosis. This research addresses the challenge of accurately classifying and segmenting GI images for real-time applications, where traditional methods often struggle due to the diversity and complexity of abnormalities. The high computational demands of this domain require efficient and adaptable solutions. This PhD thesis presents a multifaceted approach to GI image analysis. Initially, texture-based feature extraction and classification methods were explored, achieving high processing speed (over 4000 FPS) and strong performance (F1-score: 0.76, Accuracy: 0.98) on the Kvasir V2 dataset. The study then transitions to deep learning, where an optimized model combined with data bagging techniques improved performance, reaching an accuracy of 0.92 and an F1-score of 0.60 on the HyperKvasir dataset, and an F1-score of 0.88 on Kvasir V2. To support real-time detection, a streamlined neural network integrating texture and local binary patterns was developed. By addressing inter-class similarity and intra-class variation through a learned threshold, the system achieved 41 FPS with high accuracy (0.99) and an F1-score of 0.91 on HyperKvasir. Additionally, two segmentation tools are proposed to enhance usability, leveraging Depth-Wise Separable Convolution and neural network ensembles for improved detection, particularly in low-FPS scenarios. Overall, this research introduces novel and adaptable methodologies, progressing from traditional texture-based techniques to deep learning and ensemble approaches, providing a comprehensive framework for advancing GI image analysis. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on Kvasir V1](): Image Classification; Kvasir V1; Accuracy: 99. - [GI tract pathology classification on GI tract (test)](): GI tract pathology classification; GI tract (test); True Positives: 435. - [Abnormality classification and landmarks detection on Hyper Kvasir V3](): Abnormality classification and landmarks detection; Hyper Kvasir V3; Accuracy: 99. - [Classification of abnormalities and landmarks detection on Kvasir V2](): Classification of abnormalities and landmarks detection; Kvasir v2; Accuracy: 99. ## Original sources and implementation [Original paper]() [PDF]()