Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Autor: Krishnamoorthy S; Zhejiang Bioinformatics International Science and Technology Cooperation Center, Wenzhou-Kean University, Wenzhou, Zhejiang Province, China.; Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics, Wenzhou-Kean University, Wenzhou, Zhejiang Province, China., Zhang Y; Department of Neurology, Wenzhou Central Hospital Medical Group, Wenzhou 325000, China., Kadry S; Department of Applied Data Science, Noroff University College, Kristiansand, Norway.; Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, UAE.; Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon., Khan MA; Department of Computer Science, HITEC University, Taxila, Pakistan., Alhaisoni M; Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia., Mustafa N; Wenzhou-Kean University, School of Science and Technology, Wenzhou, Zhejiang Province, China., Yu W; Wenzhou-Kean University, School of Science and Technology, Wenzhou, Zhejiang Province, China., Alqahtani A; College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
Jazyk: angličtina
Zdroj: Computational intelligence and neuroscience [Comput Intell Neurosci] 2023 Feb 21; Vol. 2023, pp. 4776770. Date of Electronic Publication: 2023 Feb 21 (Print Publication: 2023).
DOI: 10.1155/2023/4776770
Abstrakt: Malfunctions in the immune system cause multiple sclerosis (MS), which initiates mild to severe nerve damage. MS will disturb the signal communication between the brain and other body parts, and early diagnosis will help reduce the harshness of MS in humankind. Magnetic resonance imaging (MRI) supported MS detection is a standard clinical procedure in which the bio-image recorded with a chosen modality is considered to assess the severity of the disease. The proposed research aims to implement a convolutional neural network (CNN) supported scheme to detect MS lesions in the chosen brain MRI slices. The stages of this framework include (i) image collection and resizing, (ii) deep feature mining, (iii) hand-crafted feature mining, (iii) feature optimization with firefly algorithm, and (iv) serial feature integration and classification. In this work, five-fold cross-validation is executed, and the final result is considered for the assessment. The brain MRI slices with/without the skull section are examined separately, presenting the attained results. The experimental outcome of this study confirms that the VGG16 with random forest (RF) classifier offered a classification accuracy of >98% MRI with skull, and VGG16 with K-nearest neighbor (KNN) provided an accuracy of >98% without the skull.
Competing Interests: The authors declare that they have no conflicts of interest.
(Copyright © 2023 Sujatha Krishnamoorthy et al.)
Databáze: MEDLINE
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