Streamlined Lung Abnormality Identification with Explainable AI: A Unified Framework for Chest CT and X-Ray Analysis

Based on CT and chest X-ray images, a new automatic classification method for lung anomaly identification is introduced. The issue of an unbalanced and limited available dataset is addressed by data augmentation. Various preprocessing methods are implemented to eliminate noise. Original CCT model serves as a foundation of this model and it is undergoing an ablation study with a 32 x 32 image CT scan dataset for best configuration determination. The model is then trained on an X-ray dataset for testing its performance in different modalities and performance is compared with six pre-trained models. Classic models showed mediocre outcomes with test accuracies between 40% and 78% for CT and 50% to 75% for X-ray with a large number of training cycles. On the other hand, the proposed model showed outstanding performance with test accuracies of 99.70% for CT and 95.30% for X-ray. By progressively shrinking the dataset of training images, the robustness of the model is assured, demonstrating that the model will still be able to perform well despite the reduction in data. The decisions of the model are explained through the explainable AI method Grad-CAM, which creates color visualizations to enable medical practitioners to make swift and certain judgments. This paper solves issues with training time and computational complexity by integrating deep learning methods with image preprocessing to identify lung abnormalities.