Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912400193617920 |
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| author | Hazlett, Alanna Ohashi, Naomi Rodriguez, Timothy Adewole, Sodiq |
| author_facet | Hazlett, Alanna Ohashi, Naomi Rodriguez, Timothy Adewole, Sodiq |
| contents | In this work, we investigate the performance across multiple classification models to classify chest X-ray images into four categories of COVID-19, pneumonia, tuberculosis (TB), and normal cases. We leveraged transfer learning techniques with state-of-the-art pre-trained Convolutional Neural Networks (CNNs) models. We fine-tuned these pre-trained architectures on a labeled medical x-ray images. The initial results are promising with high accuracy and strong performance in key classification metrics such as precision, recall, and F1 score. We applied Gradient-weighted Class Activation Mapping (Grad-CAM) for model interpretability to provide visual explanations for classification decisions, improving trust and transparency in clinical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_22609 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method Hazlett, Alanna Ohashi, Naomi Rodriguez, Timothy Adewole, Sodiq Image and Video Processing Computer Vision and Pattern Recognition Machine Learning In this work, we investigate the performance across multiple classification models to classify chest X-ray images into four categories of COVID-19, pneumonia, tuberculosis (TB), and normal cases. We leveraged transfer learning techniques with state-of-the-art pre-trained Convolutional Neural Networks (CNNs) models. We fine-tuned these pre-trained architectures on a labeled medical x-ray images. The initial results are promising with high accuracy and strong performance in key classification metrics such as precision, recall, and F1 score. We applied Gradient-weighted Class Activation Mapping (Grad-CAM) for model interpretability to provide visual explanations for classification decisions, improving trust and transparency in clinical applications. |
| title | Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2505.22609 |