A Novel Continuous-time Hidden Markov Model Based on a Bag of Features Extracted from MR Brain Images for Alzheimer's Stage Progression and Detection

Background: Mounting novel solutions for conspicuous neurodegenerative disorders that grow consistently, such as Alzheimer's disease, rely on tracking and identifying disease development, improvement, and progression. Compared to many clinical or survey-based detection methods, early Alzheimer's stage detection can be possible through computer-based MR brain images and discrete stochastic processes.

Aim: In the case of Alzheimer's stage progression, the existing models illustrate that the learning problem comprises two issues: estimating posterior probabilities of the Alzheimer's stage and computing conditioned statistics of the Alzheimer's end-stage. The proposed model overcomes these issues by restructuring the estimation problem as EM-centered CT- HMM.

Methods: This paper proposes a novel framework model with two phases; the first phase covers the feature extraction of magnetic resonance imaging based on many computer vision methods known as a collection of bag-of-features (BoF). In the second phase, the EM-centered learning method is used for the continuous-time hidden Markov model (CT-HMM), an efficient approach to modeling Alzheimer's disease progression with time and stages. The proposed CT-HMM is implemented with eight Alzheimer's stages (source: ADNI) to visualize and predict the stage progression of the ADNI MRI dataset.

Results: The proposed model reported the transition posterior probability as 0.765 (high to low stage progression) and 0.234 (low to high stage progression). The model's accuracy and F1 score are estimated as 97.13 and 96.51, respectively.

Conclusion: The proposed model's accuracy and evaluation metrics reported higher results in the work on Alzheimer's stage progression and prediction.