2020
DOI: 10.3390/app10113723
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Abstract: Voice pathology disorders can be effectively detected using computer-aided voice pathology classification tools. These tools can diagnose voice pathologies at an early stage and offering appropriate treatment. This study aims to develop a powerful feature extraction voice pathology detection tool based on Deep Learning. In this paper, a pre-trained Convolutional Neural Network (CNN) was applied to a dataset of voice pathology to maximize the classification accuracy. This study also proposes a distinguished tra… Show more

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Cited by 124 publications
(56 citation statements)
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“…At an early point the authors in (33) provided a tool can diagnose voice pathologies and suggesting a suitable treatment. Developing a strong feature detection method for voice pathology processing based on the deep Learning (33). In order to obtain better performance for a transparent, continuous, and ubiquitous system, the integration of cloud technology with the Internet of Things is necessary in the healthcare industry; IoT has several uses, one of which is Speech Pathology Control (34).…”
Section: Related Workmentioning
confidence: 99%
“…At an early point the authors in (33) provided a tool can diagnose voice pathologies and suggesting a suitable treatment. Developing a strong feature detection method for voice pathology processing based on the deep Learning (33). In order to obtain better performance for a transparent, continuous, and ubiquitous system, the integration of cloud technology with the Internet of Things is necessary in the healthcare industry; IoT has several uses, one of which is Speech Pathology Control (34).…”
Section: Related Workmentioning
confidence: 99%
“…Cloud computing is convenient for integrating data on the cloud, making it easier to update medical records. Moreover, cloud computing provides a large number of resources that can accommodate huge datasets of biomedical images or speech data [ 7 ]. A critical feature of cloud computing is the high availability of the services that can help healthcare industries provide uninterrupted services with less downtime [ 8 ].…”
Section: Introductionmentioning
confidence: 99%
“…We utilized a variety of assessment measures that include accuracy, precision, f1score, recall, and execution time to evaluate the performance of the proposed algorithms during the identification and classification of conflict flows in terms of efficiency and effectiveness. These evaluation measurements are computed as shown in Equations (10)(11)(12)(13)(14).…”
Section: Experiments Results and Discussionmentioning
confidence: 99%
“…On the other hand, the techniques for detecting and classifying flow conflicts in SDN models are highly imperative. For example, Machine Learning (ML) algorithms have proved their efficiency and effectiveness in detecting and classifying two or more subjects [5], [6], with application in several domains such as identification of spam emails [7], images classification in the medical domain [8], [9], voice pathology detection [10]- [12], and language identification [13], [14]. In these methods, the implemented ML algorithms played the main role.…”
Section: Introductionmentioning
confidence: 99%