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%.

2019冠状病毒病（COVID-19）大流行对全球构成了重大挑战，数以百万计的人受到影响，数百万人的生命陨落。本研究提出了一种注重隐私的COVID-19早期检测方法，该方法利用呼吸声音和胸部X射线图像。通过区块链和优化神经网络，所提出的方法确保了数据的安全性和准确性。胸部X射线图像通过先进的预处理、分割和特征提取技术进行处理。同时，呼吸声音通过三高斯滤波器和梅尔频率倒谱系数特征进行处理。音频和图像特征的融合是通过渐进式分割变形场融合模块实现的。所提出的双采样扩张预激活残差注意力卷积神经网络（DSPANN）通过增强蛇优化降低了计算复杂度，同时提升了分类精度。此外，还实施了一种以隐私为中心的基于区块链的加密哈希联邦算法，以实现安全的全球模型训练。这一综合方法不仅应对了COVID-19检测的挑战，而且优先考虑了医疗保健应用中的数据隐私。所提出的框架展现了98%的识别准确率、97.02%的特异性和98%的敏感性。