Abnormalities and Disease Detection in Gastro-Intestinal Tract Images

2026-03-23

AI Summary

This work proposes an efficient analysis framework for real-time classification and segmentation of lesions in gastrointestinal imaging, addressing the challenges posed by their high diversity and complexity. The approach integrates texture features—including Local Binary Patterns—with a lightweight deep learning architecture. A learnable thresholding mechanism is introduced to mitigate inter-class similarity and intra-class variation, while techniques such as data bagging, depth-wise separable convolutions, and ensemble strategies collectively enhance model generalization and inference efficiency. Experimental results on the Kvasir V2 and HyperKvasir datasets demonstrate the method’s effectiveness, achieving 98% accuracy with an F1-score of 0.91 on Kvasir V2 and 99% accuracy at 41 frames per second on HyperKvasir, confirming both its practical utility and robust performance in real-time clinical settings.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Natural Language Processing: Text Classification & Sentiment Analysis

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Gastrointestinal image analysis
• abnormality detection
• real-time classification
• image segmentation
• disease detection

Innovation

• real-time GI image analysis
• texture and LBP fusion
• learned threshold for intra-class variation
• Depth-Wise Separable Convolution
• neural network ensemble