CoronaHack-Respiratory-Sound-Dataset

The COronaVIrus Disease of 2019 (COVID19) pandemic poses a significant global challenge, with millions

affected and millions of lives lost. This study introduces a privacy conscious approach for early detection of COVID19,

employing breathing sounds and chest X-ray images. Leveraging Blockchain and optimized neural networks, proposed

method ensures data security and accuracy. The chest X-ray images undergo preprocessing, segmentation and feature

extraction using advanced techniques. Simultaneously, breathing sounds are processed through tri-gaussian filters and mel

frequency cepstral coefficient features. The fusion of audio and image features are achieved with a progressive split

deformable field fusion module. The proposed Dual Sampling dilated Pre-activation residual Attention convolution Neural

Network (DSPANN) enhances classification accuracy while reducing computational complexity through augmented snake

optimization. Furthermore, a privacy-centric blockchain-based encrypted crypto hash federated algorithm is implemented for

secure global model training. This comprehensive approach not only addresses COVID-19 detection challenges but also

prioritizes data privacy in healthcare applications. The proposed framework exhibited recognition accuracy rates of 98%,

specificity of 97.02%, and sensitivity of 98%.