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- W4285289413 abstract "Brain magnetic resonance imaging (MRI) is required for patients having brain disease symptoms. These diseases include but are not limited to Parkinson’s disease, dementia, Alzheimer’s disease, autism spectrum disorder, etc. Each of these diseases can be detected using novel classification and prediction techniques which are suited for MRI data. These techniques use image segmentation, feature extraction, feature selection and classification algorithms in order to effectively detect the presence of diseases. A large number of research-works are present which use different algorithm combinations for effective classification of these diseases. While designing any such system, researchers need to carefully identify and use algorithms that can perform this classification task with high accuracy. In this text, an empirical analysis of feature extraction, feature selection and classification algorithms on different diseases are done, which can assist researchers and system designers to select the most optimally performing algorithms for their system design. Moreover, this text also describes the nuances of these techniques, and recommends best practices to improve their overall performance. Using the statistical analysis done in this text, researchers can also quantify and predict the performance of their system prior to real-time implementation." @default.
- W4285289413 created "2022-07-14" @default.
- W4285289413 creator A5011336928 @default.
- W4285289413 creator A5026199390 @default.
- W4285289413 date "2022-01-01" @default.
- W4285289413 modified "2023-09-29" @default.
- W4285289413 title "Empirical Analysis of Magnetic Resonance Imaging-Based Brain Disease Analysis Systems: A Statistical Perspective" @default.
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- W4285289413 doi "https://doi.org/10.1007/978-981-16-7985-8_77" @default.
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