# Enhanced Multi-Class Classification of Gastrointestinal Endoscopic Images with Interpretable Deep Learning Model

arXiv: https://arxiv.org/abs/2503.00780 · HTML: https://mlsanity.com/paper/2503.00780/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 43 references that are in the map.
- First posted on arXiv 2 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [Big Neural Networks Waste Capacity](https://mlsanity.com/paper/1301.3583/) (2013)
- [A Critical Appraisal of Data Augmentation Methods for Imaging-Based Medical Diagnosis Applications](https://mlsanity.com/paper/2301.02181/) (2022)
- [Deep-learning models in medical image analysis: Detection of esophagitis from the Kvasir Dataset](https://mlsanity.com/paper/2301.02390/) (2023)

## Built on by (most cited first, 1 of 1)

- [CFMD: Dynamic Cross-layer Feature Fusion for Salient Object Detection](https://mlsanity.com/paper/2504.01326/) (2025)

## Abstract (arXiv)

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.