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Alz-ConvNets for Classification of Alzheimer Disease Using Transfer Learning Approach

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Abstract

Alzheimer disease (AD) is a progressive brain disorder that gradually deprives patients of their basic abilities. Despite the absence of specific treatments, early detection of this neurodegenerative disease can slow its progression and prevent further deterioration. AD can manifest in different stages, including non-demented (nD), mildly demented (mD), moderately demented (moD), and very mildly demented (vmD) which are taken from the Kaggle repository naming [Alzheimer dataset (4 class of Images)]. To classify these stages, we utilized various convolutional network models such as Alz-DenseConvNet, Alz-ResConvNet, Alz-VGGConvNet, Alz-MobileConvNet, and Alz-XceptionConvNet. Both binary and multiclass classification is performed to classify the different stages of AD. After testing and validation, Alz-MobileConvNet was found to be the superior method for multiclass classification with an accuracy (Acc) of 94%. On the other hand, Alz-VGGConvNet surpassed other methods for binary classification with an effective Acc of 99%.

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Data Availability

The data set is available on online Kaggle Repository. The link for the Repository is https://www.kaggle.com/tourist55/alzheimers-dataset-4-class-of-images.

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Authors and Affiliations

  1. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India

    Amar Shukla, Rajeev Tiwari & Shamik Tiwari

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  1. Amar Shukla
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Correspondence to Amar Shukla.

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This article is part of the topical collection “Cyber Security and Privacy in Communication Networks” guest edited by Rajiv Misra, R K Shyamsunder, Alexiei Dingli, Natalie Denk, Omer Rana, Alexander Pfeiffer, Ashok Patel and Nishtha Kesswani.

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Shukla, A., Tiwari, R. & Tiwari, S. Alz-ConvNets for Classification of Alzheimer Disease Using Transfer Learning Approach. SN COMPUT. SCI. 4, 404 (2023). https://doi.org/10.1007/s42979-023-01853-7

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