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An Optimal Weighted Ensemble of 3D CNNs for Early Diagnosis of Alzheimer’s Disease

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Abstract

Alzheimer disease (AD) is a chronic neurological disorder in which the loss of brain cells causes dementia. Early and accurate diagnosis of AD will lead to better treatment of the disease before irreversible brain damage has been occurred. This paper proposes the classification of Alzheimer's disease using 3D structural Magnetic Resonance Imaging (sMRI) images through 3D convolutional neural networks (CNNs). Most existing methods utilizing 3D subject-level CNNs for Alzheimer's disease classification design a single model which relies on a very large training dataset for improved generalization. Herein, we address this issue through 3D transfer learning which makes use of knowledge gained from a pre-trained task. We train 3D versions of five classical 2D image classification architectures—ResNet, ResNeXt, SE-ResNet, SE-ResNeXt, and SE-Net—by initializing each model with pre-trained weights from their 2D counterparts, and combine their predictions through a weighted average method. The weights assigned to each model of the ensemble are optimized to achieve a performance better than any single 3D CNN model. With a relatively smaller training dataset, the proposed model obtains 97.27%, 82.33%, 90.41%, 84.22%, 84.26%, and 77.1% accuracies for the Alzheimer’s disease (AD) versus cognitively normal (CN), early mild cognitive impairment (EMCI) versus CN, late mild cognitive impairment (LMCI) versus CN, EMCI versus AD, LMCI versus AD, and EMCI versus LMCI classification tasks, outperforming current state-of-the-art methods, and indicating the effectiveness of our proposed model.

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All data generated or analyzed during this study are included in this published article.

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Authors and Affiliations

  1. Department of Computer Science and Automation, Indian Institute of Science, Bangalore, India

    Sriram Dharwada

  2. Department of Computer Science and Engineering, Indian Institute of Information Technology, Nagpur, Maharashtra, 441108, India

    Jitendra Tembhurne & Tausif Diwan

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  1. Sriram Dharwada
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Correspondence to Jitendra Tembhurne.

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Dharwada, S., Tembhurne, J. & Diwan, T. An Optimal Weighted Ensemble of 3D CNNs for Early Diagnosis of Alzheimer’s Disease. SN COMPUT. SCI. 5, 252 (2024). https://doi.org/10.1007/s42979-023-02581-8

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