Optimization Based Deep Learning for COVID-19 Detection Using Respiratory Sound Signals
Authors: Jawad Ahmad Dar, Kamal Kr Srivastava, Sajaad Ahmed Lone

The COVID-19 prediction process is more indispensable to handle the spread and death occurred rate because of COVID-19. However, early and precise prediction of COVID-19 is more difficult, because of different sizes and resolutions of input image. Thus, these challenges and problems experienced by traditional COVID-19 detection methods are considered as major motivation to develop SJHBO-based Deep Q Network. The classification issue of respiratory sound has perceived a great focus from the clinical scientists as well as the community of medical researcher in the previous year for the identification of COVID-19 disease. The major contribution of this research is to design an effectual COVID-19 detection model using devised SJHBO-based Deep Q Network. In this paper, the COVID-19 detection is carried out by the deep learning with optimization technique, namely Snake Jaya Honey Badger Optimization (SJHBO) algorithm-driven Deep Q Network. Here, the SJHBO algorithm is the incorporation of Jaya Honey Badger Optimization (JHBO) along with Snake optimization (SO). Here, the COVID-19 is detected by the Deep Q Network wherein the weights of Deep Q Network are tuned by the SJHBO algorithm. Moreover, JHBO is modelled by hybrids, which are the Jaya algorithm and Honey Badger Optimization (HBO) algorithm. Furthermore, the features, such as spectral contrast, Mel frequency cepstral coefficients (MFCC), empirical mode decomposition (EMD) algorithm, spectral flux, fast Fourier transform (FFT), spectral roll-off, spectral centroid, zero-crossing rate, root mean square energy, spectral bandwidth, spectral flatness, power spectral density, mobility complexity, fluctuation index and relative amplitude, are mined for enlightening the detection performance. The developed method realized the better performance based on the accuracy, sensitivity and specificity of 0.9511, 0.9506 and 0.9469. All test results are validated with the k-fold cross validation method in order to make an assessment of the generalizability of these results. Statistical analysis is performed to analyze the performance of the proposed method based on testing accuracy, sensitivity and specificity. Hence, this paper presents the newly devised SJHBO-based Deep Q-Net for COVID-19 detection. This research considers the audio samples as an input, which is acquired from the Coswara dataset. The SJHBO-based Deep Q network approach is developed for COVID-19 detection. The developed approach can be extended by including other hybrid optimization algorithms as well as other features that can be extracted for further improving the detection performance. The proposed COVID-19 detection method is useful in various applications, like medical and so on. Developed SJHBO-enabled Deep Q network for COVID-19 detection: An effective COVID-19 detection technique is introduced based on hybrid optimization–driven deep learning model. The Deep Q Network is used for detecting COVID-19, which classifies the feature vector as COVID-19 or non-COVID-19. Moreover, the Deep Q Network is trained by devised SJHBO approach, which is the incorporation of Jaya Honey Badger Optimization (JHBO) along with Snake optimization (SO).