Mohammed, Mazin AbedAbdulkareem, Karrar HameedMostafa, Salama A.Ghani, Mohd Khanapi AbdMaashi, Mashael S.García-Zapirain, BegoñaOleagordia Ruiz, IbonAlhakami, HosamAl-Dhief, Fahad Taha2026-03-182026-03-182020-05-27Mohammed, M. A., Abdulkareem, K. H., Mostafa, S. A., Ghani, M. K. A., Maashi, M. S., Garcia-Zapirain, B., Oleagordia, I., Alhakami, H., & Al-Dhief, F. T. (2020). Voice pathology detection and classification using convolutional neural network model. Applied Sciences (Switzerland), 10(11). https://doi.org/10.3390/APP1011372310.3390/APP10113723https://hdl.handle.net/20.500.14454/5506Voice 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 training method combined with various training strategies in order to generalize the application of the proposed system on a wide range of problems related to voice disorders. The proposed system has tested using a voice database, namely the Saarbrucken voice database (SVD). The experimental results show the proposed CNN method for speech pathology detection achieves accuracy up to 95.41%. It also obtains 94.22% and 96.13% for F1-Score and Recall. The proposed system shows a high capability of the real-clinical application that offering a fast-automatic diagnosis and treatment solutions within 3 s to achieve the classification accuracy.eng© 2020 by the authorsConvolutional neural networkResidual network (ResNet34)Saarbrücken voice databaseThe vowel /a/Voice pathology classificationVoice pathology detectionjournal article2026-03-182076-3417