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An intelligent healthcare system for detection and classification to discriminate vocal fold disorders

Ali, Zulfiqar and Hossain, M Shamim and Muhammad, Ghulam and Sangaiah, Arun Kumar (2018) 'An intelligent healthcare system for detection and classification to discriminate vocal fold disorders.' Future Generation Computer Systems, 85. 19 - 28. ISSN 0167-739X

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The growing population of senior citizens around the world will appear as a big challenge in the future and they will engage a significant portion of the healthcare facilities. Therefore, it is necessary to develop intelligent healthcare systems so that they can be deployed in smart homes and cities for remote diagnosis. To overcome the problem, an intelligent healthcare system is proposed in this study. The proposed intelligent system is based on the human auditory mechanism and capable of detection and classification of various types of the vocal fold disorders. In the proposed system, critical bandwidth phenomena by using the bandpass filters spaced over Bark scale is implemented to simulate the human auditory mechanism. Therefore, the system acts like an expert clinician who can evaluate the voice of a patient by auditory perception. The experimental results show that the proposed system can detect the pathology with an accuracy of 99.72%. Moreover, the classification accuracy for vocal fold polyp, keratosis, vocal fold paralysis, vocal fold nodules, and adductor spasmodic dysphonia is 97.54%, 99.08%, 96.75%, 98.65%, 95.83%, and 95.83%, respectively. In addition, an experiment for paralysis versus all other disorders is also conducted, and an accuracy of 99.13% is achieved. The results show that the proposed system is accurate and reliable in vocal fold disorder assessment and can be deployed successfully for remote diagnosis. Moreover, the performance of the proposed system is better as compared to existing disorder assessment systems.

Item Type: Article
Uncontrolled Keywords: Healthcare, Vocal fold disorders, Binary classification, Critical bands, Auditory perception
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 09 Apr 2020 09:26
Last Modified: 09 Apr 2020 09:26

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