Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method

• URL: http://arxiv.org/abs/2505.22609v1
• Date: Wed, 28 May 2025 17:24:33 GMT
• Title: Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method
• Authors: Alanna Hazlett, Naomi Ohashi, Timothy Rodriguez, Sodiq Adewole,
• Abstract summary: We investigate the performance across multiple classification models to classify chest X-ray images.<n>We fine-tuned these pre-trained architectures on a labeled medical x-ray images.<n>The initial results are promising with high accuracy and strong performance in key classification metrics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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