CoronaHack-Respiratory-Sound-Dataset

The COronaVIrus Disease of 2019 (COVID19) pandemic poses a significant global challenge, with millionsaffected 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, proposedmethod ensures data security and accuracy. The chest X-ray images undergo preprocessing, segmentation and featureextraction using advanced techniques. Simultaneously, breathing sounds are processed through tri-gaussian filters and melfrequency cepstral coefficient features. The fusion of audio and image features are achieved with a progressive splitdeformable field fusion module. The proposed Dual Sampling dilated Pre-activation residual Attention convolution NeuralNetwork (DSPANN) enhances classification accuracy while reducing computational complexity through augmented snakeoptimization. Furthermore, a privacy-centric blockchain-based encrypted crypto hash federated algorithm is implemented forsecure global model training. This comprehensive approach not only addresses COVID-19 detection challenges but alsoprioritizes data privacy in healthcare applications. The proposed framework exhibited recognition accuracy rates of 98%,specificity of 97.02%, and sensitivity of 98%.