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Machine Learning Based Stroke Segmentation and Classification from CT-Scan: A Survey

  • Conference paper
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Pan-African Conference on Artificial Intelligence (PanAfriConAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2068))

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  • 38 Accesses

Abstract

Brain stroke is a life-threatening condition that requires early diagnosis to reduce permanent disability and death. The Computed Tomography (CT) scan is used as a gold standard technique to diagnose brain stroke. However, immediate and accurate interpretation of the images is challenging, even for skilled neuroradiologists. Therefore, researchers have focused on developing machine learning (ML) and deep learning (DL) based systems that are used to detect brain stroke from CT scan images. The main aim of this study is to review the state-of-the-art approaches that are used to perform segmentation and classification tasks, the efficiency of existing ML techniques in stroke diagnosis, the availability of public brain stroke CT scan image datasets, noises that affect brain CT scan images and denoising techniques, and limitations and challenges of ML techniques in segmentation and classification of brain stroke. A total of 33 papers were identified using inclusion and exclusion criteria from the results of 7 databases (Science-Direct, MDPI, Google Scholar, IEEE, Wiley online library, Springer Link, and Dove Press) from 2018 to June 2023. Where most of the studies utilized DL-based segmentation and classification techniques. Among the various segmentation techniques used, U-net and U-net-based models are the dominantly used techniques that exhibit superior performance over other segmentation models. In contrast, the modified 3D U-Net with the integration of the squeeze and excitation block has shown the highest Dice score. Similarly, Convolutional neural networks (CNN) based architectures were found to be dominantly used for brain stroke image classification. Furthermore, in this review, 5 publicly available brain stroke CT scan image datasets were found. 3 of them have masks and can be used to train segmentation models. However, due to the limitation in the subtypes of the images and the number of data that are available in the repositories to train ML models, most of the reviewed studies have used local data repositories.

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Authors and Affiliations

  1. Ethiopian Artificial Intelligence Institute, 40782, Addis Ababa, Ethiopia

    Elbetel Taye Zewde, Yehualashet Megersa Ayano, Taye Girma Debelee & Degaga Wolde Feyisa

  2. Department of Computer Engineering, Addis Ababa Science and Technology University, 120611, Addis Ababa, Ethiopia

    Mersibon Melese Motuma & Taye Girma Debelee

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  1. Ethiopian Artificial Intelligence Instit, Addis Adaba, Ethiopia

    Taye Girma Debelee

  2. HAWK University of Applied Sciences and Arts, Göttingen, Germany

    Achim Ibenthal

  3. Universität Ulm, Ulm, Germany

    Friedhelm Schwenker

  4. Ethiopian Artificial Intelligence Instit, Addis Ababa, Ethiopia

    Yehualashet Megersa Ayano

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Taye Zewde, E., Melese Motuma, M., Megersa Ayano, Y., Girma Debelee, T., Wolde Feyisa, D. (2024). Machine Learning Based Stroke Segmentation and Classification from CT-Scan: A Survey. In: Debelee, T.G., Ibenthal, A., Schwenker, F., Megersa Ayano, Y. (eds) Pan-African Conference on Artificial Intelligence. PanAfriConAI 2023. Communications in Computer and Information Science, vol 2068. Springer, Cham. https://doi.org/10.1007/978-3-031-57624-9_1

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