Enhanced Multi-Class Classification of Gastrointestinal Endoscopic Images with Interpretable Deep Learning Model
Journal:
arXiv
Published Date:
Mar 2, 2025
Abstract
Endoscopy serves as an essential procedure for evaluating the
gastrointestinal (GI) tract and plays a pivotal role in identifying GI-related
disorders. Recent advancements in deep learning have demonstrated substantial
progress in detecting abnormalities through intricate models and data
augmentation methods.This research introduces a novel approach to enhance
classification accuracy using 8,000 labeled endoscopic images from the Kvasir
dataset, categorized into eight distinct classes. Leveraging EfficientNetB3 as
the backbone, the proposed architecture eliminates reliance on data
augmentation while preserving moderate model complexity. The model achieves a
test accuracy of 94.25%, alongside precision and recall of 94.29% and 94.24%
respectively. Furthermore, Local Interpretable Model-agnostic Explanation
(LIME) saliency maps are employed to enhance interpretability by defining
critical regions in the images that influenced model predictions. Overall, this
work highlights the importance of AI in advancing medical imaging by combining
high classification accuracy with interpretability.
