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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Nirmala Bai Limba Naik
The Oxford College of Engineering affiliated to Visvesvaraya Technological University
Computer Science & IT Engineering

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Explainable deep learning framework for classifying pathogen-induced chest infections with fuzzy severity reasoning Nirmala Bai Limba Naik; Manju Devi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4755-4765

Abstract

Accurate classification of pathogen-induced chest infections is a vital step in enabling rapid diagnosis in the context of a high clinical burden scenario. This paper proposes an explainable deep learning (DL) framework that employs a hybrid approach by integrating an efficient classification model with both decision-level and feature-level interpretability. A neural search architecture is adopted to develop a fine-tuned EfficientNet model for multi-class classification of chest radiographs subjected to seven classes. To enhance clinical trust, a Mamdani-type fuzzy-logic-based severity reasoning layer is introduced to provide contextual assessments of disease severity and possible co-infections. Furthermore, feature-level explanations are enabled using gradient-weighted class activation mapping (Grad-CAM) to visualize important regions that contribute to each prediction. The model is validated on a comprehensive dataset, and the experimental outcome demonstrated the effectiveness of the proposed framework with classification accuracy of 96% and sensitivity of 95%.
Co-Authors Manju Devi