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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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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