The obtained results using the proposed framework are superior to previous techniques since we simultaneously considered the seven different abnormal respiratory sound classes. ANA makes sound features enough reach to be identified more accurately than the respiratory sounds without ANA. The proposed framework contains an adaptive mechanism of adding a similar type of noise to unhealthy respiratory sounds. Spectrum analysis was done through Artificial Noise Addition (ANA) in conjunction with different deep convolutional neural networks (CNN) to classify the seven abnormal respiratory sounds-both continuous (CAS) and discontinuous (DAS). In this research, we have applied Fourier analysis for the visual inspection of abnormal respiratory sounds. Lung disease is the third most ordinary cause of death worldwide, so it is essential to classify the RS abnormality accurately to overcome the death rate. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios by utilizing the typical stethoscope, which is usually considered a cheap and secure method for examining the patients. Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology, and it gives symptomatic data regarding a patient's lung. 5Department of Computer and Network Engineering, College of Information Technology, UAE University, Al Ain, United Arab Emirates.4Cloud Computing and Applications Reseach Lab, Staffordshire University, Stoke-on-Trent, United Kingdom.3Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent, United Kingdom.2Department of Information Technology, College of Computing and Information Technology at Khulais, University of Jeddah, Jeddah, Saudi Arabia.1Faculty of Computing and Information Technology, University of Gujrat, Gujrat, Pakistan.Existing challenges, open issues and future trends will be discussed as well.Rizwana Zulfiqar 1 Fiaz Majeed 1 Rizwana Irfan 2 Hafiz Tayyab Rauf 3 Elhadj Benkhelifa 4 Abdelkader Nasreddine Belkacem 5 * A summary of the main surveyed methods together with the classification parameters and used features for the sake of comparison is given. The analysis includes detection and/or classification of the sound events. The methods are shortly described and the analyzing algorithms are explained. Relevant sounds related to abnormal respiratory activities are considered as well. Such adventitious sounds include cough, wheeze, crackle and, snore. In this paper, the smartphone-based systems for automatic detection and classification of the adventitious lung sounds are surveyed. Ubiquity of smartphones and widespread use of the corresponding applications make such a device an attractive platform for hosting the detection and classification systems for adventitious lung sounds. Such systems can be presented as different platforms like medical devices, standalone software or smartphone application. Detection and classification of adventitious acoustic lung sounds plays an important role in diagnosing, monitoring, controlling and, caring the patients with lung diseases.
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