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Image-based state-of-the-art techniques for the identification and classification of brain diseases: a review

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Abstract

Detection and classification methods have a vital and important role in identifying brain diseases. Timely detection and classification of brain diseases enable an accurate identification and effective management of brain impairment. Brain disorders are commonly most spreadable diseases and the diagnosing process is time-consuming and highly expensive. There is an utmost need to develop effective and advantageous methods for brain diseases detection and characterization. Magnetic resonance imaging (MRI), computed tomography (CT), and other various brain imaging scans are used to identify different brain diseases and disorders. Brain imaging scans are the efficient tool to understand the anatomical changes in brain in fast and accurate manner. These different brain imaging scans used with segmentation techniques and along with machine learning and deep learning techniques give maximum accuracy and efficiency. This paper focuses on different conventional approaches, machine learning and deep learning techniques used for the detection, and classification of brain diseases and abnormalities. This paper also summarizes the research gap and problems in the existing techniques used for detection and classification of brain disorders. Comparison and evaluation of different machine learning and deep learning techniques in terms of efficiency and accuracy are also highlighted in this paper. Furthermore, different brain diseases like leukoariaosis, Alzheimer’s, Parkinson’s, and Wilson’s disorder are studied in the scope of machine learning and deep learning techniques.

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Dr. Li Kang is a recipient of a grant from the National Natural Science Foundation of China Grant Number 81960312.

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Authors and Affiliations

  1. Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen, China

    Ejaz Ul Haq, Jianjun Huang & Li Kang

  2. ATR Key Laboratory, Shenzhen University, Shenzhen, China

    Ejaz Ul Haq, Jianjun Huang & Li Kang

  3. Fujian Normal University, Fuzhou, China

    Hafeez Ul Haq

  4. Imaging Department, Affiliated Hospital of Zunyi Medical University, Zunyi, China

    Tijiang Zhan

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  1. Ejaz Ul Haq
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Haq, E.U., Huang, J., Kang, L. et al. Image-based state-of-the-art techniques for the identification and classification of brain diseases: a review. Med Biol Eng Comput 58, 2603–2620 (2020). https://doi.org/10.1007/s11517-020-02256-z

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