Rehman, Mujeeb Ur ORCID: https://orcid.org/0000-0002-4228-385X, Shafique, Arslan ORCID: https://orcid.org/0000-0001-7495-2248, Khan, Imdad Ullah, Ghadi, Yazeed Yasin, Ahmad, Jawad, Alshehri, Mohammed S., Al Qathrady, Mimonah, Alhaisoni, Majed and Zayyan, Muhammad H. ORCID: https://orcid.org/0000-0001-5366-9347 (2023) An efficient deep learning model for brain tumour detection with privacy preservation. CAAI Transactions on Intelligence Technology.

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Abstract

Internet of medical things (IoMT) is becoming more prevalent in healthcare applications as a result of current AI advancements, helping to improve our quality of life and ensure a sustainable health system. IoMT systems with cutting‐edge scientific capabilities are capable of detecting, transmitting, learning and reasoning. As a result, these systems proved tremendously useful in a range of healthcare applications, including brain tumour detection. A deep learning‐based approach for identifying MRI images of brain tumour patients and normal patients is suggested. The morphological‐based segmentation method is applied in this approach to separate tumour areas in MRI images. Convolutional neural networks, such as LeNET, MobileNetV2, Densenet and ResNet, are tested to be the most efficient ones in terms of detection performance. The suggested approach is applied to a dataset gathered from several hospitals. The effectiveness of the proposed approach is assessed using a variety of metrics, including accuracy, specificity, sensitivity, recall and F‐score. According to the performance evaluation, the accuracy of LeNET, MobileNetV2, Densenet, ResNet and EfficientNet is 98.7%, 93.6%, 92.8%, 91.6% and 91.9%, respectively. When compared to the existing approaches, LeNET has the best performance, with an average of 98.7% accuracy.

Item Type:	Article
Additional Information:	** Article version: VoR ** From Wiley via Jisc Publications Router ** History: received 09-02-2023; rev-recd 18-05-2023; accepted 23-05-2023; epub 01-07-2023. ** Licence for VoR version of this article: http://creativecommons.org/licenses/by/4.0/
Status:	Published
DOI:	10.1049/cit2.12254
School/Department:	School of Science, Technology and Health
URI:	https://ray.yorksj.ac.uk/id/eprint/8144

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