Capsule Vision 2024 Challenge, Multi-Class Abnormality Classification for Video Capsule Endoscopy

Video Capsule Endoscopy (VCE) presents a significant challenge, particularly due to the vast and unstructured data generated through the GI tract which may lead to difficulties in realtime analysis and classification. To address these issues, this study presents a semi-supervised methodology for image classification that leverages texture-based features. This method initially addresses the imbalance in the provided dataset and utilizes advanced texture-based feature extraction techniques such as Gray Level Co-Occurrence Matrix, Local Binary Pattern, and deep features derived from VGG16. Then, a semi-supervised learning approach, Transductive Learning Algorithm has been carried out that strengthens the model's robustness and ability to classify the normal and abnormal classes, yielding improved accuracy. Additional classification models like Random Forest and K-Means Clustering are also carried out for comparison study. The Gray Level Co-Occurrence Matrix with transductive learning outperformed the other approaches in accurately classifying the images by achieving an effective accuracy of 95.14% with 0.99 mean AUC. In conclusion, our approach which has been carried out achieved 23 rd rank in the Capsule Vision 2024 Challenge, paves the way for enhancing diagnostic capabilities, ultimately contributing to more accurate and timely identification of gastrointestinal conditions.