Comparative Study of AI Models for Automated Tuberculosis Detection Using Image Processing Techniques

Title

Comparative Study of AI Models for Automated Tuberculosis Detection Using Image Processing Techniques

Creator

Khakha, Asim Yash; Poonia, Ramesh Chandra

Description

Tuberculosis is a critical global health issue, particularly in resource-limited regions where early and accurate diagnosis is important and is in need so that the treatment is effective and the control transmission is controlled. The known diagnostic methods, such as sputum smear microscopy and nucleic acid amplification tests are costly, time-consuming, and require trained professionals. Due this in some cases it is inaccessible in many regions. Deep learning-based automated TB detection offers a promising alternative by enhancing diagnostic efficiency through medical imaging analysis. This study presents a comparative evaluation of five deep learning models, InceptionResNetV2, DenseNet, VGG16, ANN, and a custom CNN, trained on a dataset of 3,008 chest radiograph images, evenly distributed between TB-positive and normal cases. The dataset underwent advanced preprocessing techniques, pixel normalization, and data augmentation. The hyperparameter tuning process was applied, which optimized the learning rates, dropout rates, convolutional filter sizes, and batch sizes to enhance model performance. The models were assessed using accuracy, precision, recall, F1-score, sensitivity, specificity.. Experimental results indicated that the custom CNN achieved the highest classification accuracy (99.51). The superior performance of the custom CNN over other models is attributed to optimized feature extraction, effective preprocessing, and structured hyperparameter tuning. A comparative analysis with previous studies highlights how this approach mitigates dataset limitations and improves model interpretability, and the potential of AI-driven TB detection, enhancing future diagnostic efficiency by improving model generalizability and deployment in real-world healthcare settings. 2025 IEEE.

Source

Proceedings of 2025 IEEE International Conference on Contemporary Computing and Communications, InC4 2025;

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

chronic infectious disease; detection of tuberculosis (TB); Digital image processing techniques; Medical practice; Mycobacterium tuberculosis

Coverage

Khakha A.Y., Christ University, Department of Computer Science, Delhi NCR, India; Poonia R.C., Christ University, Department of Computer Science, Delhi NCR, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833152118-9;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/InC465408.2025.11256377

https://www.scopus.com/pages/publications/105030338937?origin=resultslist