Alzheimer's disease diagnosis based on feature extraction using optimised crow search algorithm and deep learning

Inspired by the fundamentals of biological evolution, bio-inspired algorithms are becoming increasingly popular for developing robust optimization techniques. These metaheuristic algorithms, unlike gradient descent methods, are computationally more efficient and excel in handling higher order multi-dimensional and non-linear. OBJECTIVES: To understand the hybrid Bio-inspired algorithms in the domain of Medical Imaging and its challenges of hybrid bio-inspired feature selection techniques. METHOD: The primary research was conducted using the three major indexing database of Scopus, Web of Science and Google Scholar. RESULT: The primary research included 198 articles, after removing the 103 duplicates, 95 articles remained as per the criteria. Finally 41 articles were selected for the study. CONCLUSION: We recommend that further research in the area of bio-inspired algorithms based feature selection in the field of diagnostic imaging and clustering. Additionally, there is a need to further investigate the use of Deep Learning hybrid models integrating the bio-inspired algorithms to include the strengths of each models that enhances the overall hybrid model.

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Alzheimer's disease diagnosis based on feature extraction using optimised crow search algorithm and deep learning

Cited by 2 publications

References 0 publications

Bio-inspired computing algorithms in dementia diagnosis – a application-oriented review

Bio-inspired computing algorithms in dementia diagnosis – a application-oriented review

Hybrid bio-inspired computing in medical image data analysis: A review

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