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Thacker, Chintan B.
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Computer Science & IT Electrical & Electronics Engineering

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Multi-class pneumonia detection using fine-tuned vision transformer model Trivedi, Khushboo; Thacker, Chintan B.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3996-4003

Abstract

Distinguishing between the various forms of pneumonia (bacterial, viral, fungal, and normal) using chest X-rays is a major problem in global health. Conventional approaches to pneumonia identification frequently depend on laborious and error-prone manual interpretation. Current machine learning (ML) models, like convolutional neural networks (CNNs), have demonstrated some success, but they frequently fail on jobs requiring multi-class classification or generalization. The potential of vision transformer (ViT) models, fine-tuned to address these limitations, is explored. The approach enhances the accuracy of pneumonia classification into four distinct classes by leveraging the attention mechanism in vision transformers (ViTs). Fine-tuning with a tagged chest X-ray dataset improves the algorithm's ability to detect subtle variations in pneumonia types. The findings demonstrate the model's effectiveness in multi-class pneumonia diagnosis, achieving a significant performance improvement with 98% accuracy across the four classes. This work highlights the promise of vision transformers in medical imaging, enabling the development of improved and scalable pneumonia classification methods.
Co-Authors Trivedi, Khushboo