–Built and implemented a custom CNN model for multi-disease classification of Chest X-Ray images. –Enhanced model robustness through Chest X-Ray specific data augmentation and L2 regularization, while employing focal loss to mitigate class imbalance. –Attained F1: 0.92 & Recall: 0.95 and F1: 0.91 & Recall: 0.94 for Tuberculosis and Pneumonia respectively. –Applied Grad-CAM heatmaps to demonstrate model alignment with radiological expertise, increasing trust in AIdriven diagnosis.