| Autor: |
Asha Latha Thandu, Pradeepini Gera |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 12, Pp 89968-89985 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3418202 |
| Popis: |
The CoronaVirus Disease of 2019 (COVID19) pandemic poses a significant global challenge, with millions affected and millions of lives lost. This study presents a privacy-conscious technique to early identification of COVID19 using breathing sounds and chest X-ray images. Using Blockchain and optimized neural networks, the suggested solution provides data confidentiality and accuracy. Chest X-ray pictures are preprocessed, segmented, and feature extracted using modern algorithms. Breathing sounds are treated simultaneously using tri-gaussian filters and mel frequency cepstral coefficient features. A progressive split deformable field fusion module combines audio and visual characteristics. The proposed Dual Sampling dilated Pre-activation residual Attention convolution Neural Network (DSPANN) improves classification accuracy while decreasing computational complexity using augmented snake optimization. Furthermore, a privacy-focused blockchain-based encrypted crypto hash federated method is used for safe global model training. This complete method overcomes COVID-19 detection issues while simultaneously prioritizing data privacy in healthcare applications. The proposed framework exhibited recognition accuracy rates of 98%, specificity of 97.02%, and sensitivity of 98%. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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