Convolutional Neural Network-Based Framework for the Detection of Tuberculosis

Ikenna Caesar Nwandu; Francisca Onyinyechi  Nwokoma; Aondowase James  Orban

doi:10.58190/imiens.2025.153

Authors

Ikenna Caesar Nwandu Software Engineering Department, Federal University of Technology Owerri, Nigeria https://orcid.org/0000-0001-6834-1088
Francisca Onyinyechi Nwokoma Computer Science Department, Federal University of Technology Owerri, Nigeria. https://orcid.org/0009-0005-3624-0131
Aondowase James Orban Software Engineering Department, University of Szeged, Szeged, Hungary https://orcid.org/0009-0003-2208-417X

DOI:

https://doi.org/10.58190/imiens.2025.153

Keywords:

chest X-ray, computer-aided diagnosis, Convolutional Neural Networks, machine learning, tuberculosis detection

Abstract

Tuberculosis (TB) remains a significant public health issue worldwide, especially in low- and middle-income countries, where access to accurate and rapid diagnostic tools is limited. Early diagnosis and treatment are essential to control spread and improve patient outcomes. Traditional TB diagnosis methods, such as sputum microscopy and culture, are time consuming and require specialized laboratory facilities. In this study, we explored the application of machine learning techniques in automating and enhancing TB detection, focusing on the analysis of chest radiograph images (X-ray). Hence, a Convolutional Neural Network-based framework is presented. The framework used advanced image preprocessing and augmentation techniques to enhance feature learning and mitigate data set imbalance to support early screening and clinical decision making. The system demonstrated high precision, correctly identifying 97% of normal chest x-rays and achieving a perfect 100% precision for TB cases, which means no false positives were recorded. In terms of recall, the model correctly detected all normal X-rays but misclassified 5% of TB cases as normal, resulting in a 95% recall for TB detection. The F1-score, which balances precision and recall, was 98% for both normal and TB cases, indicating strong classification performance. Additionally, the macro and weighted averages were both 98%, reflecting consistent and reliable model performance across different case distributions. The results indicate that the proposed CNN-based framework provides a robust, scalable, and cost-effective solution for automated TB detection, offering potential integration into computer-aided diagnostic systems.

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