Lung Disease Detection Using Gradient-Weighted Class Activation Mapping (Grad-CAM)

Wan Sofiyah, Benny Sukma Negara, Muhammad Irsyad, Iwan Iskandar, Febi Yanto

Sari


Early detection of respiratory diseases such as Coronavirus Disease-19 (Covid-19) and Pneumonia is crucial for accelerating treatment and preventing more serious complications. This study proposes a method for classifying Chest X-ray (CXR) images using a Convolutional Neural Network (CNN) to distinguish between Covid-19, Pneumonia, and normal lungs. Model training involved exploring various hyperparameter combinations to find the optimal configuration. The best results were achieved with a learning rate of 0.001, 50 epochs, and a batch size of 32, yielding an accuracy of 96.33%. Evaluation was conducted using accuracy, precision, recall, F1-score, and confusion matrix metrics. This study uses Gradient-Weighted Class Activation Mapping (Grad-CAM) as a transparent interpretation tool for model decisions. The main contribution of this study is the application of Grad-CAM in multi-class CXR classification to enhance model interpretability in lung disease diagnosis.

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DOI: http://dx.doi.org/10.30811/jaise.v5i2.7041

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