Deep Learning-Based Multi-Class Classification of Chest X-Rays for Common Pulmonary Diseases
Keywords:
Chest X-Ray, Deep Learning, Convolutional Neural Network, Medical Image Classification, Pulmonary Disease DetectionAbstract
Objective: To develop and evaluate a deep learning-based system for the automated multi-class classification of four common pulmonary diseases: pneumonia, fibrosis, edema, and nodules from chest X-ray images.
Methodology: A convolutional neural network (CNN) model was designed and trained on a subset of 4,000 images from the NIH dataset. The architecture leveraged transfer learning from a ResNet50 backbone, pre-trained on ImageNet, and was augmented with a custom classifier head. The model was compiled with the Adam optimizer and Categorical Cross-Entropy loss, and used a stratified 70-15-15 split for training, validation, and testing.
Results: The proposed model achieved an overall test accuracy of 86%. Performance varied by class, with nodules achieving the highest F1 Score (62%), while edema and pneumonia showed lower recall. The macro-averaged F1-score was 33%, reflecting the challenge of class imbalance and visual similarity between fibrosis and nodules, as evidenced by the confusion matrix.
Conclusion: The study demonstrates that a CNN model with transfer learning can effectively perform multi-class classification of pulmonary diseases in chest X-rays. The performance also highlights the diagnostic difficulty of certain conditions.
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