Deep learning-assisted IoMT framework for cerebral microbleed detection

Zeeshan Ali; Sheneela Naz; Sadaf Yasmin; Maryam Bukhari; Mucheol Kim

doi:10.1016/j.heliyon.2023.e22879

Deep learning-assisted IoMT framework for cerebral microbleed detection

Heliyon. 2023 Nov 25;9(12):e22879. doi: 10.1016/j.heliyon.2023.e22879. eCollection 2023 Dec.

Authors

Zeeshan Ali¹, Sheneela Naz², Sadaf Yasmin³, Maryam Bukhari³, Mucheol Kim⁴

Affiliations

¹ Research and Development Setups, National University of Computer and Emerging Sciences, Islamabad, 44000, Pakistan.
² Department of Computer Science, COMSATS University Islamabad, Islamabad, 45550, Pakistan.
³ Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, 43600, Pakistan.
⁴ School of Computer Science and Engineering, Chung-Ang University, Seoul, 06974, South Korea.

Abstract

The Internet of Things (IoT), big data, and artificial intelligence (AI) are all key technologies that influence the formation and implementation of digital medical services. Building Internet of Medical Things (IoMT) systems that combine advanced sensors with AI-powered insights is critical for intelligent medical systems. This paper presents an IoMT framework for brain magnetic resonance imaging (MRI) analysis to lessen the unavoidable diagnosis and therapy faults that occur in human clinical settings for the accurate detection of cerebral microbleeds (CMBs). The problems in accurate CMB detection include that CMBs are tiny dots 5-10 mm in diameter; they are similar to healthy tissues and are exceedingly difficult to identify, necessitating specialist guidance in remote and underdeveloped medical centers. Secondly, in the existing studies, computer-aided diagnostic (CAD) systems are designed for accurate CMB detection, however, their proposed approaches consist of two stages. Potential candidate CMBs from the complete MRI image are selected in the first stage and then passed to the phase of false-positive reduction. These pre-and post-processing steps make it difficult to build a completely automated CAD system for CMB that can produce results without human intervention. Hence, as a key goal of this work, an end-to-end enhanced UNet-based model for effective CMB detection and segmentation for IoMT devices is proposed. The proposed system requires no pre-processing or post-processing steps for CMB segmentation, and no existing research localizes each CMB pixel from the complete MRI image input. The findings indicate that the suggested method outperforms in detecting CMBs in the presence of contrast variations and similarities with other normal tissues and yields a good dice score of 0.70, an accuracy of 99 %, as well as a false-positive rate of 0.002 %. © 2017 Elsevier Inc. All rights reserved.

Keywords: Cerebral microbleed (CMB) segmentation; Computer-aided diagnostic (CAD) systems; Deep learning; Internet of medical things; UNet.