| Autor: |
Verma, Amar Kumar, Saurabh, Prerna, Shah, Deep Madhukant, Inturi, Vamsi, Sudha, Radhika, Rajasekharan, Sabareesh Geetha, Soundrapandiyan, Rajkumar |
| Zdroj: |
Proceedings of the Institution of Mechanical Engineers -- Part H -- Journal of Engineering in Medicine (Sage Publications, Ltd.); Dec2024, Vol. 238 Issue 11/12, p1133-1145, 13p |
| Abstrakt: |
This investigation attempts to propose a novel Wavelet and Local Binary Pattern-based Xception feature Descriptor (WLBPXD) framework, which uses a deep-learning model for classifying chronic infection amongst other infections. Chronic infection (COVID-19 in this study) is identified via RT-PCR test, which is time-consuming and requires a dedicated laboratory (materials, equipment, etc.) to complete the clinical results. X-rays and computed tomography images from chest scans offer an alternative method for identifying chronic infections. It has been demonstrated that chronic infection can be diagnosed from X-ray images acquired in a real-world setting. The images are transformed using the discrete wavelet transform (DWT), combined with the local binary pattern (LBP) technique. Pre-trained deep-learning models, such as AlexNet, Xception, VGG-16 and Inception Resnet50, extract the features. Subsequently, the extracted features are fused using feature-fusion approaches and subjected to classification. The AlexNet, in conjunction with the DWT model, produced 99.7% accurate results, whereas the AlexNet and the LBP model produced 99.6% accurate results. Therefore, the proposed method is efficient as it offers a better detection accuracy and eventually enhances the scope of early detection, thus assisting the clinical perspectives. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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