Multi-Class Classification and Segmentation on Kvasir Endoscopic Images Using Deep Learning Methods
TL;DR
The results suggest that running both models in parallel on the same input image could provide simultaneous classification and segmentation outputs, offering more comprehensive diagnostic information compared to single-task approaches.
Abstract
This study evaluates the use of deep learning methods for multi-class classification and polyp segmentation on Kvasir endoscopic images. A dual-model approach was employed, where EfficientNet handles multi-class classification and U-Net handles polyp segmentation, each trained and evaluated independently on their respective datasets. The EfficientNet-B0 model achieved high performance, with accuracy, precision, recall, and F1-score values exceeding 91%, demonstrating its effectiveness in detecting various gastrointestinal abnormalities across eight classes. The U-Net model, while showing strong performance in background detection, faced challenges in lesion delineation, achieving a Dice Similarity Coefficient (DSC) of 34.50% and IoU of 20.85%. These results suggest that running both models in parallel on the same input image could provide simultaneous classification and segmentation outputs, offering more comprehensive diagnostic information compared to single-task approaches. This study contributes to the understanding of independent deep learning components that could support AI-based medical decision-making in gastrointestinal endoscopy.