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On Disharmony in Batch Normalization and Dropout Methods for Early Categorization of Alzheimer’s Disease

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  • Ahsan Bin Tufail

    (Department of Computer Science, National University of Sciences and Technology, Balochistan Campus, Quetta 87300, Pakistan
    School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
    These authors contributed equally to this work.)

  • Inam Ullah

    (BK21 Chungbuk Information Technology Education and Research Center, Chungbuk National University, Cheongju 28644, Korea
    These authors contributed equally to this work.)

  • Ateeq Ur Rehman

    (Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan)

  • Rehan Ali Khan

    (Department of Electrical Engineering, University of Science and Technology Bannu, Bannu 28100, Pakistan)

  • Muhammad Abbas Khan

    (Department of Electrical Engineering, FICT, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta 87300, Pakistan)

  • Yong-Kui Ma

    (School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Nadar Hussain Khokhar

    (Department of Civil Engineering, National University of Sciences and Technology, Balochistan Campus, Quetta 87300, Pakistan)

  • Muhammad Tariq Sadiq

    (School of Architecture, Technology and Engineering, University of Brighton, Brighton BN2 4AT, UK)

  • Rahim Khan

    (School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Muhammad Shafiq

    (Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea)

  • Elsayed Tag Eldin

    (Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt)

  • Nivin A. Ghamry

    (Faculty of Computers and Artificial Intelligence, Cairo University, Giza 3750010, Egypt)

Abstract

Alzheimer’s disease (AD) is a global health issue that predominantly affects older people. It affects one’s daily activities by modifying neural networks in the brain. AD is categorized by the death of neurons, the creation of amyloid plaques, and the development of neurofibrillary tangles. In clinical settings, an early diagnosis of AD is critical to limit the problems associated with it and can be accomplished using neuroimaging modalities, such as magnetic resonance imaging (MRI) and positron emission tomography (PET). Deep learning (DL) techniques are widely used in computer vision and related disciplines for various tasks such as classification, segmentation, detection, etc. CNN is a sort of DL architecture, which is normally useful to categorize and extract data in the spatial and frequency domains for image-based applications. Batch normalization and dropout are commonly deployed elements of modern CNN architectures. Due to the internal covariance shift between batch normalization and dropout, the models perform sub-optimally under diverse scenarios. This study looks at the influence of disharmony between batch normalization and dropout techniques on the early diagnosis of AD. We looked at three different scenarios: (1) no dropout but batch normalization, (2) a single dropout layer in the network right before the softmax layer, and (3) a convolutional layer between a dropout layer and a batch normalization layer. We investigated three binaries: mild cognitive impairment (MCI) vs. normal control (NC), AD vs. NC, AD vs. MCI, one multiclass AD vs. NC vs. MCI classification problem using PET modality, as well as one binary AD vs. NC classification problem using MRI modality. In comparison to using a large value of dropout, our findings suggest that using little or none at all leads to better-performing designs.

Suggested Citation

  • Ahsan Bin Tufail & Inam Ullah & Ateeq Ur Rehman & Rehan Ali Khan & Muhammad Abbas Khan & Yong-Kui Ma & Nadar Hussain Khokhar & Muhammad Tariq Sadiq & Rahim Khan & Muhammad Shafiq & Elsayed Tag Eldin &, 2022. "On Disharmony in Batch Normalization and Dropout Methods for Early Categorization of Alzheimer’s Disease," Sustainability, MDPI, vol. 14(22), pages 1-22, November.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14695-:d:966454
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    References listed on IDEAS

    as
    1. Ahsan Bin Tufail & Yong-Kui Ma & Mohammed K. A. Kaabar & Ateeq Ur Rehman & Rahim Khan & Omar Cheikhrouhou, 2021. "Classification of Initial Stages of Alzheimer’s Disease through Pet Neuroimaging Modality and Deep Learning: Quantifying the Impact of Image Filtering Approaches," Mathematics, MDPI, vol. 9(23), pages 1-16, December.
    2. Supreet Kaur & Sandeep Sharma & Ateeq Ur Rehman & Elsayed Tag Eldin & Nivin A. Ghamry & Muhammad Shafiq & Salil Bharany, 2022. "Predicting Infection Positivity, Risk Estimation, and Disease Prognosis in Dengue Infected Patients by ML Expert System," Sustainability, MDPI, vol. 14(20), pages 1-20, October.
    3. Bashir Khan Yousafzai & Sher Afzal Khan & Taj Rahman & Inayat Khan & Inam Ullah & Ateeq Ur Rehman & Mohammed Baz & Habib Hamam & Omar Cheikhrouhou, 2021. "Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network," Sustainability, MDPI, vol. 13(17), pages 1-21, August.
    4. Amit Sundas & Sumit Badotra & Salil Bharany & Ahmad Almogren & Elsayed M. Tag-ElDin & Ateeq Ur Rehman, 2022. "HealthGuard: An Intelligent Healthcare System Security Framework Based on Machine Learning," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
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