Alzheimer's diagnosis using deep learning in segmenting and classifying 3D brain MR images

• Type: Article in International Scientific Journal
• Date: 2020-07

Authors

Tran Anh Tuan (Author) Other; Teh Bao Pham (Author) Other; Jin Young Kim (Author) Other; João Manuel R. S. Tavares (Author) FEUP

Journal

Title: International Journal of Neuroscience

Vol. 132 No. 7

Pages: 689-698

ISSN: 0020-7454

Publisher: Taylor & Francis

Indexing

ISI Web of Knowledge - 0 Citations

ISI Web of Science

Scopus - 2 Citations

Scientific classification

FOS: Medical and Health sciences

CORDIS: Technological sciences

Other information

Authenticus ID: P-00T-00N

DOI: 10.1080/00207454.2020.1835900

Abstract (EN): Background and objectives Dementia is one of the brain diseases with serious symptoms such as memory loss, and thinking problems. According to the World Alzheimer Report 2016, in the world, there are 47 million people having dementia and it can be 131 million by 2050. There is no standard method to diagnose dementia, and consequently unable to access the treatment effectively. Hence, the computational diagnosis of the disease from brain Magnetic Resonance Image (MRI) scans plays an important role in supporting the early diagnosis. Alzheimer's Disease (AD), a common type of Dementia, includes problems related to disorientation, mood swings, not managing self-care, and behavioral issues. In this article, we present a new computational method to diagnosis Alzheimer's disease from 3D brain MR images. Methods An efficient approach to diagnosis Alzheimer's disease from brain MRI scans is proposed comprising two phases: I) segmentation and II) classification, both based on deep learning. After the brain tissues are segmented by a model that combines Gaussian Mixture Model (GMM) and Convolutional Neural Network (CNN), a new model combining Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM) is used to classify Alzheimer's disease based on the segmented tissues. Results We present two evaluations for segmentation and classification. For comparison, the new method was evaluated using the AD-86 and AD-126 datasets leading to Dice 0.96 for segmentation in both datasets and accuracies 0.88, and 0.80 for classification, respectively. Conclusion Deep learning gives prominent results for segmentation and feature extraction in medical image processing. The combination of XGboost and SVM improves the results obtained.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10