International Journal of Advanced Engineering Application

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

Author(s):Dangete Suma1, Malladi Sneha2, Mohammad Shafi3, Shaik Subhani4, Anabathula Mohith5

Affiliation: 1Assistant Professor, 2,3,4,5Student 1,2,3,4,5Department of Computer Science & Engineering / Dhanekula Institute of Engineering and Technology / India

Page No: 21-27

Volume issue & Publishing Year: Volume 2 Issue 5 ,May-2025

Journal: International Journal of Advanced Engineering Application (IJAEA)

ISSN NO: 3048-6807

DOI: https://doi.org/10.5281/zenodo.17677968

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Article Indexing:

Abstract:
The CBAM-Efficient Net Model integrates the Convolutional Block Attention Module(CBAM) with the Efficient Net
architecture for better focus on relevant regions of the images for precise detection of tuberculosis (TB) from chest X
rays. Built from scratch with X-rays from Kaggle, it utilizes data augmentation (image compression, elastic
transformation), 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 model
compact and efficient for use in clinical settings. The system is designed to perform TB diagnosis predictions in real
time through a Flask interface with ngrok.

Keywords: TB detection, Deep Learning, CBAM, Efficient Net, Vision Transformer, Grad-CAM, Chest X-Ray.

Reference:

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