Enhanced CBAM-Efficient Net Model for Efficient Tuberculosis Diagnosis Using Chest X Ray Images

The CBAM-Efficient Net Model integrates the Convolutional Block Attention Module(CBAM) with the Efficient Netarchitecture for better focus on relevant regions of the images for precise detection of tuberculosis (TB) from chest Xrays. Built from scratch with X-rays from Kaggle, it utilizes data augmentation (image compression, elastictransformation), contrastive learning, and advanced feature extraction to enhance performance. In the final stage,Vision Transformers in a hybrid architecture improves the models accuracy. In addition to significance visualization,Grad-CAM offers clinicians an attention visualization. Post-training quantization and pruning help keep the modelcompact and efficient for use in clinical settings. The system is designed to perform TB diagnosis predictions in realtime through a Flask interface with ngrok.