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Early diagnosis of Alzhiemer’s disease using wavelet-pooling based deep convolutional neural network

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Abstract

Coronal anatomic slices of structural MRI images clearly show the topographical structures of the Hippocampus and Amygdala, which are essential for early diagnosis of Alzheimer’s disease (AD). MR coronal sections are best appreciated for studying the complex topographical relationships of the amygdala and the topographical structures of the hippocampus, which helps in the early detection of disease. Early diagnosis helps prevent the disease’s progression to its final stage. It allows the patient to be aware of the severity of the disease and can take the necessary therapeutic medications to prevent its progression. A coronal view study of MR images is proposed in this paper for early diagnosis of disease using a wavelet-pooling-based multi-path and multi-scale convolutional neural network. This work aims to perform a three-way classification of 2D coronal slices of MRI images to diagnose Mild Cognitive Impairment, AD, and Normal Control in a single algorithm and learn the brain-affected regions through Gradcam visualization. wavelet-pooling is utilized to extract the texture details of the image and thus provide spatial attention to the texture features of the image, which is impossible using Max-pooling or Average-pooling. Multi-scale feature learning is incorporated using parallel multiple low-rank convolutional kernels to capture varying scales of atrophy regions. Multi-path mode compensates for the early loss of features and avoids vanishing gradient problems. The proposed model is trained and tested on the ADNI dataset comprising 900 subjects to give an accuracy of 96.5\(\%\) with ten-fold cross-validation. The multi-scale and multi-path methods significantly reduce the number of learnable parameters.

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Acknowledgements

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health www.fnih.org. The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Government Engineering College Wayanad, APJ Abdul Kalam Technological University, Thiruvananthapuram, Kerala, India

    Manu Raju

  2. Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tiruchirappalli, India

    Varun P. Gopi

  3. Department of Computer Science and Engineering, Government Engineering College Wayanad, APJ Abdul Kalam Technological University, Thiruvananthapuram, Kerala, India

    V S Anitha

  4. Department of Radiodiagnosis and Imaging, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, India

    Abishek Sherawat

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  1. Manu Raju
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Correspondence to Varun P. Gopi.

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Raju, M., P. Gopi, V., Anitha, V.S. et al. Early diagnosis of Alzhiemer’s disease using wavelet-pooling based deep convolutional neural network. Sādhanā 48, 173 (2023). https://doi.org/10.1007/s12046-023-02219-8

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