Brain image identification and classification on Internet of Medical Things in healthcare system using support value based deep neural network. (September 2022)

• Record Type: Journal Article
• Authors: Vankdothu, Ramdas; Hameed, Mohd Abdul; Ameen, Ayesha; Unnisa, Raheem
• Is Part Of: Computers & electrical engineering. Volume 102(2022)
• Publication Date: 2022-09
• Subjects: Skull stripping -- Geometric features -- Texture feature -- Entropy feature -- Classification; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108196
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The Internet of Medical Things (IoMT) combines the Internet of Things (IoT) with medical equipment to provide better patient comfort, cost-effective medical solutions, faster hospital treatments, and even more individualized healthcare. Brain tumors are caused by a mass of random cells in the brain, harming the brain and posing a risk. Brain image recognition is a difficult task these days. This paper explains the application of a support value based deep neural network (SDNN) in e-Health care as use of the Internet of Medical Things (IoMT) innovation for the early detection and accurate classification of cancerous cells in brain pictures. Initially, as an exploration database, photos based on IoT innovation and clinical images are collected. The input brain picture is stripped of its skull for brain area extraction during the preprocessing stage. The effective features such as entropy, geometric, and texture features are retrieved from the preprocessed output images. Finally, the suggested support value based adaptive deep neural network (SDNN) recognition distinguishes between normal and abnormal brain images into normal or abnormal images dependent on the extracted features. Experimental results show that our proposed SDNN approach accomplishes an accuracy as high as 94.30%. In the meantime, the other existing methods, CNN+ReLU is 91.02%, CNN+PReLU, CNN+BN+ReLU, CNN+BN+PReLU achieve had the worst accuracy of 82.05, 85.55, and 83.6% independently. Compared with other existing works, our proposed methodology achieves higher outcomes.