Medical Image Recognition Method for Respiratory System Viruses Diagnosis based on a Proposed Combined Neural Network System
B. Tayel, M., M. Fahmy, A. (2022). Medical Image Recognition Method for Respiratory System Viruses Diagnosis based on a Proposed Combined Neural Network System. EKB Journal Management System, 47(5), 9-15. doi: 10.21608/bfemu.2022.268308
Mazhar B. Tayel; Ahmed M. Fahmy. "Medical Image Recognition Method for Respiratory System Viruses Diagnosis based on a Proposed Combined Neural Network System". EKB Journal Management System, 47, 5, 2022, 9-15. doi: 10.21608/bfemu.2022.268308
B. Tayel, M., M. Fahmy, A. (2022). 'Medical Image Recognition Method for Respiratory System Viruses Diagnosis based on a Proposed Combined Neural Network System', EKB Journal Management System, 47(5), pp. 9-15. doi: 10.21608/bfemu.2022.268308
B. Tayel, M., M. Fahmy, A. Medical Image Recognition Method for Respiratory System Viruses Diagnosis based on a Proposed Combined Neural Network System. EKB Journal Management System, 2022; 47(5): 9-15. doi: 10.21608/bfemu.2022.268308

Medical Image Recognition Method for Respiratory System Viruses Diagnosis based on a Proposed Combined Neural Network System

MEJ- Mansoura Engineering Journal
Article 7, Volume 47, Issue 5, October 2022, Page 9-15  XML PDF (1.38 MB)
Document Type: Research Studies
DOI: 10.21608/bfemu.2022.268308
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Authors
Mazhar B. Tayel1; Ahmed M. Fahmy email 2
1is with the Alexandria University, Alexandria, Egypt. He is now a professor in Electrical Engineering department
2is a PhD student in Alexandria University, Alexandria, Egypt
Abstract
Respiratory viral detection is a confusing and time-consuming task of constantly looking at clinical pictures of patients. So, there is a need to develop and improve the respiratory case prediction model as soon as possible to control the spread of Respiratory viruses.  Today advanced machine learning methods diagnose viruses such as Corona viruses that can be effectively classified. This paper proposes a scanning model based on using a combination of CNN (Convolutional Neural Network) and PNN (Probabilistic Neural Network) to classify images of Corona viruses. The proposed combined network, (PCN) system. The images used are resized, and the light was adjusted in a way that reflects the size of the Plaque damage and highlighted by converting the image to Hue Saturation Value (HSV). Essential distance information in an image is filtered, leaving important features of the image information. The PCN system uses the Convolutional Neural Network to calculate the dependent factors and passes the results to the Probabilistic Neural Network, and link the features of intermediate stages with the combined network to predict segmentation. As a result, PCN system achieves 100% accuracy.
Keywords
Biomedical imaging; image classification; Covid-19; Probabilistic Neural Network (PNN); Convolutional Neural Network (CNN); Computed Tomography (CT scan); Artificial Intelligence (AL); Deep Learning (DL); and Machine Learning (DL)
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