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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Shivasubramanya, Jayasri Basavapatna
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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Improving COVID-19 chest X-ray classification via attention-based learning and fuzzy-augmented data diversity Cheluvaraju, Girish Shyadanahalli; Shivasubramanya, Jayasri Basavapatna
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10861

Abstract

This paper presents a hybrid deep learning (DL) framework that combines model-level and data-level enhancements to improve classification performance without compromising clinical relevance. The proposed framework consisted of an EfficientNetB0 model with a hybrid attention module, which focused attention both spatially and channel-wise, and a VGG-16 model that was trained on training data augmented using a fuzzy-logic-based contrast and brightness enhancement. The attention module focused the model by recalibrating the features in an adaptive manner. The fuzzy-logic augmentation increased data diversity while maintaining the anatomical fidelity of the medical image domain. In addition, an uncertainty-aware ensemble approach was utilized to combine both models' predictions, which considered model confidence and entropy of the predictions, to enhance the reliability of the predictions. The proposed framework achieves a classification accuracy of 99.6%, outperforming several existing approaches.
Co-Authors Cheluvaraju, Girish Shyadanahalli