Abnormalities and Disease Detection in Gastro-Intestinal Tract Images

TL;DR

This research develops efficient, adaptable methods for GI tract image analysis, combining traditional texture techniques and deep learning to improve classification, segmentation, and real-time detection in complex medical images.

Contribution

It introduces novel hybrid approaches integrating texture features, deep learning, and ensemble methods for improved GI image classification and segmentation, suitable for real-time applications.

Findings

Achieved over 4000 FPS with high accuracy using texture-based methods.

Improved deep learning models with data bagging reached 0.92 accuracy.

Developed a real-time detection system at 41 FPS with 0.99 accuracy.

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.