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Teaching learning-based brain storm optimization tuned Deep-CNN for Alzheimer’s disease classification

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Abstract

Alzheimer’s disease (AD) as an incurable disease that affects the mental functions of the patients. The early diagnosis of AD requires automatic Computer-Aided Diagnosis, which needs advanced technical practices like Deep Learning algorithms. This paper proposes a hybrid optimization tuned Deep convolutional neural network (Deep CNN) based automatic AD detection strategy. The significance of the proposed strategy lies in the optimal segmentation of Magnetic resonance imaging (MRI) and the classification of AD using the Deep CNN, which is trained using the proposed teaching learning-based brainstorm (TLBS) optimization algorithm that inherits the characteristic features of learning search agents and the associative search agents to obtain the global optimal solution for tuning the weights of the classifier. The performance evaluation using the accuracy, sensitivity, specificity, and F-Measure are obtained 97.03%, 97.18%, 97.03%, and 97.39% respectively, which shows the effectiveness of the proposed method in detecting the AD of patients.

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Authors and Affiliations

  1. Computer Science and Engineering, Institute of Aeronautical Engineering, Dundigal, Hyderabad, 500043, India

    Y. Mohana Roopa

  2. Electronics and Communication Engineering, St. Peter’s Engineering College, Telangana, Hyderabad, India

    B. Bhaskar Reddy

  3. AIA Department, Cognizant Technology Solutions India Pvt. Ltd, Hyderabad, India

    Meenigi Ramesh Babu

  4. Computer Science and Engineering, Vignan Institute of Technology and Management for Women, Hyderabad, India

    R. Krishna Nayak

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  1. Y. Mohana Roopa
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  2. B. Bhaskar Reddy
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  3. Meenigi Ramesh Babu
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  4. R. Krishna Nayak
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Roopa, Y.M., Reddy, B.B., Babu, M.R. et al. Teaching learning-based brain storm optimization tuned Deep-CNN for Alzheimer’s disease classification. Multimed Tools Appl 82, 33333–33356 (2023). https://doi.org/10.1007/s11042-023-14815-1

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  • DOI: https://doi.org/10.1007/s11042-023-14815-1

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