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A Meta-Heuristic Multi-Objective Optimization Method for Alzheimer’s Disease Detection Based on Multi-Modal Data

Author

Listed:
  • Walaa N. Ismail

    (Department of Management Information Systems, College of Business Administration, Al Yamamah University, Riyadh 11512, Saudi Arabia
    Faculty of Computers and Information, Minia University, Minia 61519, Egypt)

  • Fathimathul Rajeena P. P.

    (Computer Science Department, College of Computer Science and Information Technology, King Faisal University, Al Ahsa 400, Saudi Arabia)

  • Mona A. S. Ali

    (Computer Science Department, College of Computer Science and Information Technology, King Faisal University, Al Ahsa 400, Saudi Arabia
    Computer Science, Faculty of Computers and Artificial Intelligence, Benha University, Al Qalyubia 13511, Egypt)

Abstract

Alzheimer’s disease (AD) is a neurodegenerative disease that affects a large number of people across the globe. Even though AD is one of the most commonly seen brain disorders, it is difficult to detect and it requires a categorical representation of features to differentiate similar patterns. Research into more complex problems, such as AD detection, frequently employs neural networks. Those approaches are regarded as well-understood and even sufficient by researchers and scientists without formal training in artificial intelligence. Thus, it is imperative to identify a method of detection that is fully automated and user-friendly to non-AI experts. The method should find efficient values for models’ design parameters promptly to simplify the neural network design process and subsequently democratize artificial intelligence. Further, multi-modal medical image fusion has richer modal features and a superior ability to represent information. A fusion image is formed by integrating relevant and complementary information from multiple input images to facilitate more accurate diagnosis and better treatment. This study presents a MultiAz-Net as a novel optimized ensemble-based deep neural network learning model that incorporate heterogeneous information from PET and MRI images to diagnose Alzheimer’s disease. Based on features extracted from the fused data, we propose an automated procedure for predicting the onset of AD at an early stage. Three steps are involved in the proposed architecture: image fusion, feature extraction, and classification. Additionally, the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) is presented as a multi-objective optimization algorithm to optimize the layers of the MultiAz-Net. The desired objective functions are imposed to achieve this, and the design parameters are searched for corresponding values. The proposed deep ensemble model has been tested to perform four Alzheimer’s disease categorization tasks, three binary categorizations, and one multi-class categorization task by utilizing the publicly available Alzheimer neuroimaging dataset. The proposed method achieved (92.3 ± 5.45)% accuracy for the multi-class-classification task, significantly better than other network models that have been reported.

Suggested Citation

  • Walaa N. Ismail & Fathimathul Rajeena P. P. & Mona A. S. Ali, 2023. "A Meta-Heuristic Multi-Objective Optimization Method for Alzheimer’s Disease Detection Based on Multi-Modal Data," Mathematics, MDPI, vol. 11(4), pages 1-22, February.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:957-:d:1067127
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    References listed on IDEAS

    as
    1. Mona A. S. Ali & Fathimathul Rajeena P. P. & Diaa Salama Abd Elminaam, 2022. "An Efficient Heap Based Optimizer Algorithm for Feature Selection," Mathematics, MDPI, vol. 10(14), pages 1-33, July.
    2. Ke Niu & Jiayang Guo & Yijie Pan & Xin Gao & Xueping Peng & Ning Li & Hailong Li, 2020. "Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data," Complexity, Hindawi, vol. 2020, pages 1-9, January.
    3. Mona A. S. Ali & Fathimathul Rajeena P. P. & Diaa Salama Abd Elminaam, 2022. "A Feature Selection Based on Improved Artificial Hummingbird Algorithm Using Random Opposition-Based Learning for Solving Waste Classification Problem," Mathematics, MDPI, vol. 10(15), pages 1-34, July.
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