MRI Deep Learning-Based Solution for Alzheimer’s Disease Prediction

Abstract

Background: Alzheimer’s is a degenerative dementing disorder that starts with a mild memory impairment and progresses to a total loss of mental and physical faculties. The sooner the diagnosis is made, the better for the patient, as preventive actions and treatment can be started. Although tests such as the Mini-Mental State Tests Examination are usually used for early identification, diagnosis relies on magnetic resonance imaging (MRI) brain analysis. Methods: Public initiatives such as the OASIS (Open Access Series of Imaging Studies) collection provide neuroimaging datasets openly available for research purposes. In this work, a new method based on deep learning and image processing techniques for MRI-based Alzheimer’s diagnosis is proposed and compared with previous literature works. Results: Our method achieves a balance accuracy (BAC) up to 0.93 for image-based automated diagnosis of the disease, and a BAC of 0.88 for the establishment of the disease stage (healthy tissue, very mild and severe stage). Conclusions: Results obtained surpassed the state-of-the-art proposals using the OASIS collection. This demonstrates that deep learning-based strategies are an effective tool for building a robust solution for Alzheimer’s-assisted diagnosis based on MRI data.

An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning

“…Our work improved the sensitivity by 43.3% even when a four-class AD detection was formulated; • Specificity: The specificity was reported only in our work and [13]. Our work improved the sensitivity by 24.6%, even when a four-class AD detection was formulated; • Accuracy: Our work improved the accuracy by 21-33.0% compared with [12][13][14].…”

“…The discussion of the methodology and results of the existing works is separated based on each dataset. The works are [ 11 , 12 , 13 , 14 ] for OASIS-1, [ 15 , 16 , 17 , 18 ] for OASIS-2, and [ 19 , 20 , 21 , 22 ] for OASIS-3.…”

“…Fourclass AD detection is desired to better reflect the nature of the categorization of different types of AD; • Features and algorithms: Work [11] separated the feature extraction and AD detection into two parts using two algorithms. Our work and [12][13][14] formulated the feature extraction and AD detection with one algorithm, which is known as automatic feature extraction; • Type of cross-validation: Work [13] did not employed cross-validation that may result in insufficiency in hyperparameter tuning and evaluation of the model overfitting. Twofold cross-validation was adopted in our work and Fivefold cross-validation was used in [12,14].…”

“…Our work and [12][13][14] formulated the feature extraction and AD detection with one algorithm, which is known as automatic feature extraction; • Type of cross-validation: Work [13] did not employed cross-validation that may result in insufficiency in hyperparameter tuning and evaluation of the model overfitting. Twofold cross-validation was adopted in our work and Fivefold cross-validation was used in [12,14]. Tenfold cross-validation was used in [11], nevertheless, it was inappropriately formulated using a 80:20 ratio between the training and testing datasets; • Sensitivity: The sensitivity was reported only in our work and [13].…”

Early-Stage Alzheimer's Disease Prediction Using Machine Learning Models

“…The simplest solution is to drop the rows with missing values. The other way to fill in missing values is by Imputation ( 21 ), which refers to replacing them with their corresponding values. The model should perform better if we impute since we only have 140 measurements.…”