Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method
This work addresses chest disease diagnosis for clinical applications, but it is incremental as it applies existing transfer learning methods to a new dataset.
The paper tackled the problem of detecting chest diseases like COVID-19, pneumonia, and tuberculosis from X-ray images using deep learning, achieving high accuracy and strong performance in metrics such as precision, recall, and F1 score.
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.