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Open AccessArticle

Transfer Learning Assisted Classification and Detection of Alzheimer’s Disease Stages Using 3D MRI Scans

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Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan
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Department of Computer Science, Capital University of Science and Technology, Islamabad 45750, Pakistan
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Faculty of Engineering Sciences, Ghulam Ishaq Khan Institute, Topi 23460, Pakistan
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Department of Media Design and Technology, Faculty of Engineering & Informatics, University of Bradford; Bradford BD7 1DP, UK
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Department of Software, Sejong University, Seoul 05006, Korea
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Author to whom correspondence should be addressed.
Sensors 2019, 19(11), 2645; https://doi.org/10.3390/s19112645
Received: 5 April 2019 / Revised: 3 June 2019 / Accepted: 6 June 2019 / Published: 11 June 2019
(This article belongs to the Special Issue Edge Computing for Biomedical Signal Processing)
Alzheimer’s disease effects human brain cells and results in dementia. The gradual deterioration of the brain cells results in disability of performing daily routine tasks. The treatment for this disease is still not mature enough. However, its early diagnosis may allow restraining the spread of disease. For early detection of Alzheimer’s through brain Magnetic Resonance Imaging (MRI), an automated detection and classification system needs to be developed that can detect and classify the subject having dementia. These systems also need not only to classify dementia patients but to also identify the four progressing stages of dementia. The proposed system works on an efficient technique of utilizing transfer learning to classify the images by fine-tuning a pre-trained convolutional network, AlexNet. The architecture is trained and tested over the pre-processed segmented (Grey Matter, White Matter, and Cerebral Spinal Fluid) and un-segmented images for both binary and multi-class classification. The performance of the proposed system is evaluated over Open Access Series of Imaging Studies (OASIS) dataset. The algorithm showed promising results by giving the best overall accuracy of 92.85% for multi-class classification of un-segmented images. View Full-Text
Keywords: Alzheimer’s Detection; AlexNet; ImageNet; transfer learning; contrast stretching; K-Mean clustering Alzheimer’s Detection; AlexNet; ImageNet; transfer learning; contrast stretching; K-Mean clustering
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Maqsood, M.; Nazir, F.; Khan, U.; Aadil, F.; Jamal, H.; Mehmood, I.; Song, O.-Y. Transfer Learning Assisted Classification and Detection of Alzheimer’s Disease Stages Using 3D MRI Scans. Sensors 2019, 19, 2645.

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