Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer’s Disease-Based Neurodegenerative Disorders

Abstract

Alzheimer’s disease is incurable at the moment. If it can be appropriately diagnosed, the correct treatment can postpone the patient’s illness. To aid in the diagnosis of Alzheimer’s disease and to minimize the time and expense associated with manual diagnosis, a machine learning technique is employed, and a transfer learning method based on 3D MRI data is proposed. Machine learning algorithms can dramatically reduce the time and effort required for human treatment of Alzheimer’s disease. This approach extracts bottleneck features from the M-Net migration network and then adds a top layer to supervised training to further decrease the dimensionality and delete portions. As a consequence, the transfer network presented in this study has several advantages in terms of computational efficiency and training time savings when used as a machine learning approach for AD-assisted diagnosis. Finally, the properties of all subject slices are combined and trained in the classification layer, completing the categorization of Alzheimer’s disease symptoms and standard control. The results show that this strategy has a 1.5 percentage point better classification accuracy than the one that relies exclusively on VGG16 to extract bottleneck features. This strategy could cut the time it takes for the network to learn and improve its ability to classify things. The experiment shows that the method works by using data from OASIS. A typical transfer learning network’s classification accuracy is about 8% better with this method than with a typical network, and it takes about 1/60 of the time with this method.

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].…”

“…Only a portion of the dataset was considered in the model implementation and performance analysis in works [13,14,16,18,19]; • Some of the existing works did not employ cross-validation [13,[15][16][17][18][19][20][21][22] and improperly defined the ratio of cross-validation [11] in the performance evaluation and analysis of the AD detection models. The trained models without cross-validation may not be designed with optimal sets of hyperparameters and may be more prone to model overfitting;…”

“…Work [11] merged Class 2 and Class 3 as one class, i.e., mild/moderate AD. The remaining two works [13,14] considered a binary AD detection. 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.…”

“…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].…”