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

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 () and Nivin A. Ghamry
Additional contact information
Ahsan Bin Tufail: Department of Computer Science, National University of Sciences and Technology, Balochistan Campus, Quetta 87300, Pakistan
Inam Ullah: BK21 Chungbuk Information Technology Education and Research Center, Chungbuk National University, Cheongju 28644, Korea
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

Sustainability, 2022, vol. 14, issue 22, 1-22

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.

Keywords: neuroimaging; classification; augmentation; statistical comparison; batch normalization; dropout (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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