Version 1
: Received: 8 January 2023 / Approved: 9 January 2023 / Online: 9 January 2023 (06:58:45 CET)
How to cite:
Roshanzamir, M.; Shamsi, A.; Asgharnezhad, H.; Alizadehsani, R.; Hussain, S.; Moosaei, H.; Mohammadi, A.; Acharya, U. R.; Alinejad, H. Quantifying Uncertainty in Automated Detection of Alzheimer’s Patients Using Deep Neural Network. Preprints2023, 2023010148. https://doi.org/10.20944/preprints202301.0148.v1
Roshanzamir, M.; Shamsi, A.; Asgharnezhad, H.; Alizadehsani, R.; Hussain, S.; Moosaei, H.; Mohammadi, A.; Acharya, U. R.; Alinejad, H. Quantifying Uncertainty in Automated Detection of Alzheimer’s Patients Using Deep Neural Network. Preprints 2023, 2023010148. https://doi.org/10.20944/preprints202301.0148.v1
Roshanzamir, M.; Shamsi, A.; Asgharnezhad, H.; Alizadehsani, R.; Hussain, S.; Moosaei, H.; Mohammadi, A.; Acharya, U. R.; Alinejad, H. Quantifying Uncertainty in Automated Detection of Alzheimer’s Patients Using Deep Neural Network. Preprints2023, 2023010148. https://doi.org/10.20944/preprints202301.0148.v1
APA Style
Roshanzamir, M., Shamsi, A., Asgharnezhad, H., Alizadehsani, R., Hussain, S., Moosaei, H., Mohammadi, A., Acharya, U. R., & Alinejad, H. (2023). Quantifying Uncertainty in Automated Detection of Alzheimer’s Patients Using Deep Neural Network. Preprints. https://doi.org/10.20944/preprints202301.0148.v1
Chicago/Turabian Style
Roshanzamir, M., U. Rajendra Acharya and Hamid Alinejad. 2023 "Quantifying Uncertainty in Automated Detection of Alzheimer’s Patients Using Deep Neural Network" Preprints. https://doi.org/10.20944/preprints202301.0148.v1
Abstract
One of the most common forms of dementia is Alzheimer’s disease (AD), which leads to progressive mental deterioration. Unfortunately, there is no definitive diagnosis and cure that can stop the condition progressing. The diagnosis is often performed based on the clinical history and neuropsychological data, including magnetic resonance imaging (MRI). Deep neural networks (DNN) algorithms are gaining popularity for medical diagnosis, and have been used widely for the analysis of MRI data. DNNs can extract hidden features from thousands of training images automatically. However, they cannot judge how confident they are about their predictions. To use DNNs in safety-critical applications such as medical diagnosis, uncertainty quantification of DNNs predictions is crucial. For this purpose, Monte Carlo dropout (MCD) has been widely used, however, it may lead to overconfident and miss calibrated results. This paper proposes a framework in which the MCD algorithm’s hyper-parameters are optimized during training using Bayesian optimization for the first time. The conducted optimization leads to assigning high predictive entropy to erroneous predictions and making it possible to recognize risky predictions. The proposed framework is used for AD diagnosis, which has not been done before. We compare our method with some existing methods in the literature based on different uncertainty quantification criteria. The results of comprehensive experiments on the Kaggle dataset using a deep model pre-trained on the ImageNet dataset show that the proposed algorithm can quantify uncertainty much better than the existing methods.
Keywords
Uncertainty quantification; Deep learning, Alzheimer; MRI; MCD; Classification
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.