Singh, Deshant and Sharma, Anuraganand (2025) Improving imbalanced tuberculosis diagnosis using GoogleNet with image - based SMOTified - GAN. [Conference Proceedings]
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Tuberculosis (TB) is still a major worldwide health problem; early diagnosis and intervention depend heavily on the ability to accurately identify the illness from chest X-ray pictures. However, creating reliable and efficient diagnosis algorithms is severely hampered by the inherent class imbalance and data scarcity in medical datasets. This work presents a unique oversampling methodology called Image-based SMOTified-GAN to handle the imbalanced data difficulties in TB diagnosis. Image-based SMOTified-GAN is an attempt for augmentation of artificial samples to mitigate the lack of positive TB cases in training data, using inspiration from the Synthetic Minority Over-sampling Technique (SMOTE) and Generative Adversarial Networks (GANs). After adding these artificial samples to the training set has improved the model’s capacity to identify minute patterns that indicate the presence of TB. This paper underscores the synergistic interaction between SMOTE and GANs and elucidates the theoretical foundation of SMOTified-GAN. To provide a visual context, the study emphasises that Image-based SMOTified-GAN specifically operates on X-ray images and includes illustrative examples of how generated images resemble typical TB X-ray manifestations. Experimental results with GoogleNet demonstrate that Image-based SMOTified-GAN outperforms other oversampling techniques in terms of precision, recall and F1 score, metrics more suited to imbalanced datasets.
| Item Type: | Conference Proceedings |
|---|---|
| Additional Information: | Program schedule is available at: https://www.scrs.in/conference/adcis2024/page/ADCIS_2024%20Program%20Schedule |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | School of Information Technology, Engineering, Mathematics and Physics (STEMP) |
| Depositing User: | Anuraganand Sharma |
| Date Deposited: | 16 Dec 2025 22:17 |
| Last Modified: | 16 Dec 2025 22:17 |
| URI: | https://repository.usp.ac.fj/id/eprint/14795 |
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