HDQNN-Net: An Optimal Asthma Disease Detection Technique for Voice Signal Using Hybrid Deep Q-Neural Networks

Abstract

Recently, asthma patients are severely suffering COVID-19 disease, thus the asthma has become one of the dangerous diseases in the world. Further, asthma is occurring in all age groups, which causing huge loss to patient’s health. The primary way to detect the asthma in humans is done by their speech signals, as the asthma severity is increases, which manipulates the properties of speech signal. The conventional methods are failed to extract the maximum features from the speech signals, which resulted in low classification performance. Thus, this article is focused on implementation of real time asthma disease detection and identification technique from speech signals using Hybrid Deep Q Neural Networks (HDQNN). Initially, the features from the speech signals are extracted by using Krill herd optimization (KHO) approach, which extracts the detailed disease specific features. Further, the optimal features are extracted by using chaotic opposition krill herd optimization (COKHO) algorithm. Then, HDQNN is used to classify the type of asthma such as normal, and stridor classes. Further, COKHO is also used to optimize the losses generated in the HDQNN model. The simulation results shows that the proposed HDQNN method resulted in superior performance as compared to state of art approaches.