Image-based state-of-the-art techniques for the identification and classification of brain diseases: a review
Autor: | Li Kang, Jianjun Huang, Tijiang Zhan, Ejaz Ul Haq, Hafeez Ul Haq |
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Rok vydání: | 2020 |
Předmět: |
Magnetic Resonance Spectroscopy
Computer science 0206 medical engineering Biomedical Engineering Neuroimaging Computed tomography 02 engineering and technology Machine learning computer.software_genre 030218 nuclear medicine & medical imaging Machine Learning 03 medical and health sciences 0302 clinical medicine Image Processing Computer-Assisted medicine Humans Segmentation Brain Diseases medicine.diagnostic_test business.industry Deep learning Brain Magnetic resonance imaging Magnetic Resonance Imaging 020601 biomedical engineering Computer Science Applications Identification (information) ComputingMethodologies_PATTERNRECOGNITION Positron-Emission Tomography Artificial intelligence State (computer science) Tomography X-Ray Computed business computer Image based |
Zdroj: | Medical & Biological Engineering & Computing. 58:2603-2620 |
ISSN: | 1741-0444 0140-0118 |
Popis: | 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. |
Databáze: | OpenAIRE |
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