Multi-class diagnosis of Alzheimer’s disease using cascaded three dimensional-convolutional neural network

• Published in: Australasian physical & engineering sciences in medicine Vol. 43; no. 4; pp. 1219 - 1228
• Main Authors: Raju, Manu, Gopi, Varun P., Anitha, V. S., Wahid, Khan A.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2020; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Atrophy; Biological and Medical Physics; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Brain; Classification; Classifiers; Dementia; Diagnosis; Feature extraction; Image segmentation; Magnetic resonance imaging; Medical and Radiation Physics; Medical imaging; Neural networks; Scientific Paper; Social problems; Support vector machines; Structural MRI; Three dimensional-convolutional neural network; Alzheimer’s disease; Mild cognitive impairment
• ISSN: 2662-4729; 0158-9938; 2662-4737; 2662-4737; 1879-5447
• DOI: 10.1007/s13246-020-00924-w

Dementia is a social problem in the aging society of advanced countries. Presently, 46.8 million people affected with dementia worldwide, and it may increase to 130 million by 2050. Alzheimer’s disease (AD) is the most common form of dementia. The cost of care for AD patients in 2015 was 818 billion US dollars and is expected to increase intensely due to the increasing number of patients due to the aging society. It isn’t easy to cure AD, but early detection is crucial. This paper proposes a multi-class classification of AD, mild cognitive impairment (MCI), and normal control (NC) subjects using three dimensional-convolutional neural network with Support Vector Machine classifier. A cross-sectional study on structural MRI data of 465 subjects, including 132 AD patients, 181 MCI, and 152 NC, is performed in this paper. The highly complex and spatial atrophy patterns of the brain related to Alzheimer’s Disease and MCI are extracted from structural MRI images using cascaded layers of the three dimensional convolutional neural network. The hectic process of segmentation and further extraction of handcrafted features is eliminated. The complete image is considered for the processing, thus incorporating every region of the brain for the classification. The features extracted using four cascaded layers of three dimensional-convolutional neural network are fed into the Support Vector Machine classifier. The proposed method achieved 97.77% accuracy which outperforms state of the art, and this algorithm is a promising indicator for the diagnosis of AD.