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. 2022 May 23:2022:9092289.
doi: 10.1155/2022/9092289. eCollection 2022.

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

Suneet Gupta  1 , V Saravanan  2 , Amarendranath Choudhury  3 , Abdullah Alqahtani  4 , Mohamed R Abonazel  5 , K Suresh Babu  6
Affiliations

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

Suneet Gupta et al. Comput Math Methods Med. .

Retraction in

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.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
AD classification problem with MRI transfer learning.
Figure 2
Figure 2
Basic framework of the classification method.
Figure 3
Figure 3
Features extracted from the top layer.
Figure 4
Figure 4
Classification accuracy curves for the evaluated classification algorithms.
Figure 5
Figure 5
Accuracy of slicing methods.
Figure 6
Figure 6
Classification accuracy curves for different counts of slices.
Figure 7
Figure 7
Classification accuracy curves for different fully connected layers in a classification layer.
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