The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
IMPACT‐TB: Integrated Medical Imaging and AI for Precise Tuberculosis Detection
Open accessAbstract
ABSTRACT Tuberculosis (TB) remains one of the most significant global health challenges, particularly in low‐resource settings where timely and accurate diagnosis is critical for effective treatment and disease control. Despite advancements in diagnostic technologies, existing models often face limitations, such as high computational demands, limited generalizability across diverse populations, and challenges in interpretability. These constraints can hinder the widespread adoption of automated TB diagnosis systems, particularly in areas where the disease burden is high. To address these challenges, we propose IMPACT‐TB, an advanced deep learning‐based model that integrates cutting‐edge feature extraction using the CoAtNet architecture and a robust fully connected neural network (FCNN) for precise TB diagnosis. The key steps of IMPACT‐TB include detailed feature extraction from chest X‐rays and CT scans using CoAtNet, followed by accurate classification through the FCNN, ensuring effective handling of complex and nonlinear relationships. The model is rigorously tested across multiple datasets, including TB‐DSI, TB‐DSII, and TB‐DSIII, demonstrating consistent and superior performance with high accuracy of 96.56% for TB‐DSI, 97.45% for TB‐DSII, and 96.12% for TB‐DSIII, respectively. Compared to existing models, IMPACT‐TB not only achieves better diagnostic accuracy but also offers enhanced interpretability and generalizability, making it a valuable tool for TB diagnosis in diverse clinical settings.
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“Furthermore, multimodal systems have shown promise in delivering fairer and more equitable care, as they can incorporate broader patient contexts, reducing disparities that often arise when models are trained on limited or homogeneous data (10–12).”
From The role of multimodality in clinical disease diagnosis: advances, challenges, and opportunities (2026) · cites this paper as [10]
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Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10179-8 · Cited by 2,495 · cited 1×
“Deep learning models tend to act, especially in medical diagnosis, like “black boxes,” thereby making their decisions obscuretoclinicians[11].”
Vats, S., Sharma, V., Singh, K., Katti, A., Ariffin, M. M., Ahmad, M. N., Ahmadian, A., & Salahshour, S. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122129 · Cited by 446
Utilizing convolutional neural networks to classify monkeypox skin lesions
Eliwa, E. H. I., Koshiry, A. M. E., El‐Hafeez, T. A., & Farghaly, H. M. (2023) · Scientific Reports · doi:10.1038/s41598-023-41545-z · Cited by 175
Nafisah, S. I., & Muhammad, G. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07258-6 · Cited by 157
Sharma, V., Nillmani, Gupta, S. K., & Shukla, K. K. (2023) · Intelligent Medicine · doi:10.1016/j.imed.2023.06.001 · Cited by 124
Ameen, A., Fattoh, I. E., El‐Hafeez, T. A., & Ahmed, K. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-01011-7 · Cited by 102
Tuberculosis chest X-ray detection using CNN-based hybrid segmentation and classification approach
Iqbal, A., Usman, M., & Ahmed, Z. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104667 · Cited by 95
Ahmed, M. S., Rahman, A., AlGhamdi, F., AlDakheel, S., Hakami, H., AlJumah, A., AlIbrahim, Z., Youldash, M. M., Khan, M. A. A., & Ahmed, M. I. B. (2023) · Diagnostics · doi:10.3390/diagnostics13152562 · Cited by 89
Kazemzadeh, S., Yu, J., Jamshy, S., Pilgrim, R., Nabulsi, Z., Chen, C., Beladia, N., Lau, C. T., McKinney, S. M., Hughes, T., et al. (2022) · Radiology · doi:10.1148/radiol.212213 · Cited by 88
Fati, S. M., Senan, E. M., & ElHakim, N. (2022) · Applied Sciences · doi:10.3390/app12147092 · Cited by 72
Research works printed but not linked: 1
- 10.3233/XST210894 · DOI
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Sarwar, S., Majeed, S., Nawaz, A., Bibi, R., & Lee, S. W. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1687335 · Cited by 7
The role of multimodality in clinical disease diagnosis: advances, challenges, and opportunities
Nawaz, A., Edinat, A., Rana, M. R. R., Ali, T., Mustafa, G., Tahir, S., & Lee, S. W. (2026) · Frontiers in public health · doi:10.3389/fpubh.2026.1788454 · Cited by 3
“Furthermore, multimodal systems have shown promise in delivering fairer and more equitable care, as they can incorporate broader patient contexts, reducing disparities that often arise when models are trained on limited or homogeneous data (10–12).”
Metadata
- Authors
- Nawaz, A., Shehab, M., Rana, M. R. R., Qureshi, B., Khan, Z., & Babar, M.
- Affiliations
- Amman Arab University (JO)
- Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology (PK)
- Prince Sultan University (SA)
- Corresponding author
- Nawaz, Asif
- ORCID
- Venue
- Engineering Reports
- Publisher
- Wiley
- Published
- 2025-08
- Volume
- 7
- Issue
- 8
- ISSN
- 2577-8196 (ISSN-L)
- OpenAlex
- W4413737201
- Cited by
- 2
- References
- 32
- Citations by year
- 2026: 2
- FWCI
- 1.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Engineering Reports (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Prince Sultan University
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Infectious Diseases › Tuberculosis Research and Epidemiology
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- tuberculosis detection, chest radiography, computed tomography, CoAtNet, feature extraction, fully connected neural network, automated diagnosis, resource-limited settings, model interpretability, generalizability
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