Pyar, Kyi (2024) Segmentation Performance Analysis of Transfer Learning Models on X-Ray Pneumonia Images. Journal of Future Artificial Intelligence and Technologies, 1 (1). pp. 64-74. ISSN 3048-3719
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Abstract
Segmentation of pneumonia areas on chest X-rays is essential to improve the accuracy of recognition tasks and subsequent diagnosis. The capabilities of deep learning techniques, U-Net, SegNet, and DeepLabV3, are assessed to achieve these purposes. Using transfer learning, these models were adapted to pneumonia-specific datasets. The evaluation focuses on Intersection over Union (IoU) and accuracy metrics. Results show that DeepLabV3 outperforms U-Net and SegNet, achieving 84.4% accuracy and 81% IoU. U-Net achieves 80.3% accuracy and 68% IoU, while SegNet achieves 81.0% accuracy and 70% IoU. These findings highlight the potential of transfer learning models to automate the segmentation of pneumonia-affected regions, thereby facilitating timely and accurate medical intervention.
Item Type: | Article |
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Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Depositing User: | dl fts |
Date Deposited: | 01 Dec 2024 13:25 |
Last Modified: | 01 Dec 2024 13:25 |
URI: | https://dl.futuretechsci.org/id/eprint/92 |