Work

Interpretable Deep Learning Approaches for Reliable GI Image Classification: A Study with the HyperKvasir Dataset

Year: 2024

Type: conference-paper

Abstract: Deep learning has emerged as a promising tool for automating gastrointestinal (GI) disease diagnosis. However, multi-class GI disease classification remains underexplored. This study addresses this gap by presenting a framework that uses advanced models like InceptionNetV3 and ResNet50, combined with boosting algorithms (XGB, LGBM), to classify lower GI abnormalities. InceptionNetV3 with XGB achieved the best recall of 0.81 and an F1 score of 0.90. To assist clinicians in understanding model decisions, the Grad-CAM technique, a form of explainable AI, was employed to highlight the critical regions influencing predictions, fostering trust in these systems. This approach significantly improves both the accuracy and reliability of GI disease diagnosis.

Source: International Conference on Computer and Information Technology (ICCIT)

DOI: 10.1109/iccit64611.2024.11022387

Authors: Saif Bin Wahid, Zarin Tasnim Rothy, Raisul Kabir News, Sakib Anwar Rieyan

Institutions: Northern University Bangladesh, BRAC University, University of Delaware

Language: English

Topic: Colorectal Cancer Screening and Detection

Subfield: Oncology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W4411172079