Adewale, Olubunmi and Akinola, Joseph Folorunsho and Akindele, Orimolade and Afolabi, Segun and Shopeju, Habeeb Kehinde and Adetiba, E. and Adewale, Adeyinka A. (2024) Analysis and Measurement of Tuberculin Skin Test Induration Using Deep Neural Network. International Journal of Online and Biomedical Engineering, 20 (12). ISSN eISSN: 2626-8493
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Abstract
The World Health Organization (WHO) posited that tuberculosis (TB) is among the world’s ten greatest causes of mortality. Early case identification and timely treatment could minimize TB morbidity and death rates. This study adopts the UNets model for automatically detecting TB in subjects by using a deep neural network to assess the size of induration after tuberculin was injected into their hands. In order to do this, two neural network models were fine-tuned utilizing pre-learned weights from the 2012 ILSVRC ImageNet. Algorithms were developed to perform semantic segmentation of induration and compare it to that of a reference object of a known dimension. This was used to classify the status of the subject as either positive or negative. A series of experiments performed demonstrated that the optimal selection of neural network hyperparameters may provide a satisfactorily high F1 score of up to 0.977.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
| Depositing User: | Patricia Nwokealisi |
| Date Deposited: | 01 Nov 2024 16:51 |
| Last Modified: | 01 Nov 2024 16:51 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/18555 |
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