Deep learning-assisted IoMT framework for cerebral microbleed detection
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 int...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023100879 |
| _version_ | 1797383936325713920 |
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| author | Zeeshan Ali Sheneela Naz Sadaf Yasmin Maryam Bukhari Mucheol Kim |
| author_facet | Zeeshan Ali Sheneela Naz Sadaf Yasmin Maryam Bukhari Mucheol Kim |
| author_sort | Zeeshan Ali |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T21:28:12Z |
| format | Article |
| id | doaj.art-6b4d6889c43a4603a1384d5a5ff84d4b |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-08T21:28:12Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-6b4d6889c43a4603a1384d5a5ff84d4b2023-12-21T07:34:59ZengElsevierHeliyon2405-84402023-12-01912e22879Deep learning-assisted IoMT framework for cerebral microbleed detectionZeeshan Ali0Sheneela Naz1Sadaf Yasmin2Maryam Bukhari3Mucheol Kim4Research and Development Setups, National University of Computer and Emerging Sciences, Islamabad, 44000, PakistanDepartment of Computer Science, COMSATS University Islamabad, Islamabad, 45550, PakistanDepartment of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, 43600, PakistanDepartment of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, 43600, PakistanSchool of Computer Science and Engineering, Chung-Ang University, Seoul, 06974, South Korea; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2405844023100879Cerebral microbleed (CMB) segmentationInternet of medical thingsComputer-aided diagnostic (CAD) systemsDeep learningUNet |
| spellingShingle | Zeeshan Ali Sheneela Naz Sadaf Yasmin Maryam Bukhari Mucheol Kim Deep learning-assisted IoMT framework for cerebral microbleed detection Heliyon Cerebral microbleed (CMB) segmentation Internet of medical things Computer-aided diagnostic (CAD) systems Deep learning UNet |
| title | Deep learning-assisted IoMT framework for cerebral microbleed detection |
| title_full | Deep learning-assisted IoMT framework for cerebral microbleed detection |
| title_fullStr | Deep learning-assisted IoMT framework for cerebral microbleed detection |
| title_full_unstemmed | Deep learning-assisted IoMT framework for cerebral microbleed detection |
| title_short | Deep learning-assisted IoMT framework for cerebral microbleed detection |
| title_sort | deep learning assisted iomt framework for cerebral microbleed detection |
| topic | Cerebral microbleed (CMB) segmentation Internet of medical things Computer-aided diagnostic (CAD) systems Deep learning UNet |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023100879 |
| work_keys_str_mv | AT zeeshanali deeplearningassistediomtframeworkforcerebralmicrobleeddetection AT sheneelanaz deeplearningassistediomtframeworkforcerebralmicrobleeddetection AT sadafyasmin deeplearningassistediomtframeworkforcerebralmicrobleeddetection AT maryambukhari deeplearningassistediomtframeworkforcerebralmicrobleeddetection AT mucheolkim deeplearningassistediomtframeworkforcerebralmicrobleeddetection |