Quantifying Uncertainty in Automated Detection of Alzheimer’s Patients Using Deep Neural Network

Roshanzamir, Mohamad; Shamsi, Afshar; Asgharnezhad, Hamzeh; Alizadehsani, Roohallah; Hussain, S. Perwez; Moosaei, Hossein; Mohammadi, Arash; Acharya, U. Rajendra; Alinejad, Hamid Agha

doi:10.20944/preprints202301.0148.v1

Cited by 4 publications

(1 citation statement)

References 30 publications

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“…In one study [16], the quantification of uncertainty in deep learning-based classification for AD diagnosis was proposed. In this work, it presented a methodology for training the Monte Carlo dropout algorithm [17] by optimising its hyperparameters using Bayesian optimisation [18].…”

Section: Introductionmentioning

confidence: 99%

Alzheimer's Disease Classification Confidence of Individuals using Deep Learning on Heterogeneous Data

Alausa,

Sanchez-Bornot,

Asadpour

et al. 2024

Preprint

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Making accurate diagnosis of Alzheimer's disease (AD) is crucial for effective treatment and management. Although deep learning has been applied to AD classification, it is typically performed at group level, the data used are not sufficiently heterogeneous and comprehensive, and decision confidence is not evaluated at individual (single patient) level. This paper proposed a more practical deep learning approach that not only detects AD stages of individuals, but also provides its corresponding confidence estimation. In particular, in addition to a convolutional neural network (CNN), we incorporated a softmax confidence metric based on the network's output activity to evaluate its classification confidence. Further, we applied this approach to a heterogeneous and comprehensive data that comprised cognitive and functional assessments, tau-PET and MRI neuroimaging, medical/family history, demographic, and APoE genotype. Importantly, we utilised leave-one-out cross-validation to train the CNN and classify an individual's healthy control, mild cognitive impairment or AD state, while concurrently estimating each output decision's confidence. We showed that, over different confidence softmax temperature values, CNN could attain classification accuracies at 83-85% for the three classes while having robust confidence scores of 78-83%. Further improvement in confidence breakdown was achieved using the optimal temperature value in confidence evaluation, with higher confidence scores for correct than error decisions. Overall, the computed classification confidence of an individual may aid clinicians and other stakeholders in understanding the reliability of the model's decision outcome and offer better trust. The implication of this work may extend to other classification applications, in which the confidence level of a single deep learning-based decision can be evaluated.

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Section: Introductionmentioning

confidence: 99%

Alzheimer's Disease Classification Confidence of Individuals using Deep Learning on Heterogeneous Data

Alausa,

Sanchez-Bornot,

Asadpour

et al. 2024

Preprint

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Using Data Mining Techniques to Analyze Facial Expression Motion Vectors

Roshanzamir,

Alizadehsani,

Roshanzamir

et al. 2023

Lecture Notes in Computer Science

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An improved multi-task least squares twin support vector machine

Moosaei¹,

Bazikar²,

Pardalos³

2023

Ann Math Artif Intell

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In recent years, multi-task learning (MTL) has become a popular field in machine learning and has a key role in various domains. Sharing knowledge across tasks in MTL can improve the performance of learning algorithms and enhance their generalization capability. A new approach called the multi-task least squares twin support vector machine (MTLS-TSVM) was recently proposed as a least squares variant of the direct multi-task twin support vector machine (DMTSVM). Unlike DMTSVM, which solves two quadratic programming problems, MTLS-TSVM solves two linear systems of equations, resulting in a reduced computational time. In this paper, we propose an enhanced version of MTLS-TSVM called the improved multi-task least squares twin support vector machine (IMTLS-TSVM). IMTLS-TSVM offers a significant advantage over MTLS-TSVM by operating based on the empirical risk minimization principle, which allows for better generalization performance. The model achieves this by including regularization terms in its objective function, which helps control the model’s complexity and prevent overfitting. We demonstrate the effectiveness of IMTLS-TSVM by comparing it to several single-task and multi-task learning algorithms on various real-world data sets. Our results highlight the superior performance of IMTLS-TSVM in addressing multi-task learning problems.

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Quantifying Uncertainty in Automated Detection of Alzheimer’s Patients Using Deep Neural Network

Cited by 4 publications

References 30 publications

Alzheimer's Disease Classification Confidence of Individuals using Deep Learning on Heterogeneous Data

Alzheimer's Disease Classification Confidence of Individuals using Deep Learning on Heterogeneous Data

Using Data Mining Techniques to Analyze Facial Expression Motion Vectors

An improved multi-task least squares twin support vector machine

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