Papers › Detection of Dementia Through 3D Convolutional Neural Networks Based on Amyloid PET

Detection of Dementia Through 3D Convolutional Neural Networks Based on Amyloid PET

24 Jan 2022IEEE Symposium Series on Computational Intelligence (SSCI) 2022 1archive 2025-07-28

Giovanna Castellano, Andrea Esposito, Marco Mirizio, Graziano Montanaro, Gennaro Vessio

Dementia is one of the most common diseases in the elderly and a leading cause of mortality and disability. In recent years, a research effort has been made to develop computer aided diagnosis tools based on machine (deep) learning models fed with neuroimaging data. However, while much work has been done on MRI imaging, very little attention has been paid on amyloid PETs, which have been recently recognized to be a promising and powerful biomarker of neurodegeneration. In this paper, we contribute to this less explored research area by proposing a 3D Convolutional Neural Network aimed at detecting dementia based on amyloid PET scans. An experiment performed on the recently released OASIS-3 dataset, which provides the community with a new benchmark to advance this line of research further, yielded very promising results and provided new evidence on the effectiveness of amyloid PET.

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Tasks

Medical Image Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Medical Image Classification OASIS 3 3D CNN AUC 87% #1 of 1 Archive leaderboard report
Medical Image Classification OASIS 3 3D CNN Accuracy 83% #1 of 1 Archive leaderboard report
Medical Image Classification OASIS 3 3D CNN Sensitivity 0.86 #1 of 1 Archive leaderboard report
Medical Image Classification OASIS 3 3D CNN Specificity 86 #1 of 1 Archive leaderboard report

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Methods

3D CNN

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