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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Trivedi, Khushboo
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Computer Science & IT Electrical & Electronics Engineering 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.
Strid-CNN: moving filters with convolution neural network for multi-class pneumonia classification Trivedi, Khushboo; Bhupeshbhai Thacker, Chintan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3253-3261

Abstract

Millions of people around the world suffer from pneumonia, a serious lung illness. To effectively treat and manage this condition, a quick and accurate diagnosis is essential. This study thoroughly examines different ways of using transfer learning to classify pneumonia into multiple categories. We use well-known methods like DenseNet121, VGGNet-16, ResNet-50, and Inception Net, as well as a new method called Strid-CNN, which applies moving filters with convolution neural network. Through extensive testing, we show that each method effectively uses pre-learned information on a large dataset of medical images, accurately identifying pneumonia across various classes. Our results reveal subtle differences in performance among these methods, providing insights into how well they adapt to the challenging field of medical image analysis. Additionally, the Strid-CNN method shows promising results, indicating its potential as a competitive alternative. This research offers valuable guidance on choosing the right transfer learning approach for classifying pneumonia into multiple categories, contributing to improvements in diagnostic accuracy and healthcare effectiveness. Our study not only highlights the current state of transfer learning in pneumonia classification but also its potential to enhance clinical outcomes and patient care.
Co-Authors Bhupeshbhai Thacker, Chintan Thacker, Chintan B.