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Classification of Alzheimer’s Disease Subjects from MRI Using Deep Convolutional Neural Networks

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Novel & Intelligent Digital Systems: Proceedings of the 3rd International Conference (NiDS 2023) (NiDS 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 784))

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Abstract

The classification of Alzheimer’s disease (AD) using deep learning techniques has shown promising results. However, achieving successful application in medical settings requires a combination of high precision, short processing time, and generalizability to different populations. In this study, we propose a convolutional semantic network (CNN)-based classification algorithm that utilizes magnetic resonance imaging (MRI) scans from individuals with AD. Our models achieved average area under the curve (AUC) values of 0.91−0.94 for within- dataset recognition and 0.88−0.89 for between-dataset recognition. The proposed convolutional framework can be potentially applied to any image dataset, offering the flexibility to design a computer-aided diagnosis system targeting the prediction of various clinical conditions and neuropsychiatric disorders using multimodal imaging and tabular clinical data.

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Notes

  1. 1.

    https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images.

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Author information

Authors and Affiliations

  1. Department of Information and Communication Systems Engineering, University of the Aegean, Samos, Greece

    Orestis Papadimitriou & Athanasios Kanavos

  2. Department of Informatics and Computer Engineering, University of West Attica, Athens, Greece

    Phivos Mylonas

  3. Department of Informatics, Ionian University, Corfu, Greece

    Manolis Maragoudakis

Authors
  1. Orestis Papadimitriou
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  2. Athanasios Kanavos
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  3. Phivos Mylonas
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  4. Manolis Maragoudakis
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Corresponding author

Correspondence to Athanasios Kanavos .

Editor information

Editors and Affiliations

  1. Department of Environment, Ionian University, Zakynthos, Greece

    Katerina Kabassi

  2. Department of Informatics and Computer Engineering, University of West Attica, Egaleo, Greece

    Phivos Mylonas

  3. College of Engineering, University of the Philippines, Diliman, Philippines

    Jaime Caro

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Papadimitriou, O., Kanavos, A., Mylonas, P., Maragoudakis, M. (2023). Classification of Alzheimer’s Disease Subjects from MRI Using Deep Convolutional Neural Networks. In: Kabassi, K., Mylonas, P., Caro, J. (eds) Novel & Intelligent Digital Systems: Proceedings of the 3rd International Conference (NiDS 2023). NiDS 2023. Lecture Notes in Networks and Systems, vol 784. Springer, Cham. https://doi.org/10.1007/978-3-031-44146-2_28

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