Enhanced CBAM-Efficient Net Model for Efficient Tuberculosis Diagnosis Using Chest X Ray Images
Year: 2025
Type: article
Abstract: 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.
Author: Dangete Suma1, Malladi Sneha2, Mohammad Shafi3, Shaik Subhani4, Anabathula Mohith5
Language: English
Topic: COVID-19 diagnosis using AI
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7106234293
FWCI:
Cites:
Cited by:
Related to: