B. Vivekanandam

Work place: Faculty of Computer Science and Multimedia, Lincoln University College, Malaysia

E-mail: vivekanandam@lincoln.edu.my


Research Interests: Deep Learning, Machine Learning, Artificial Intelligence and Applications, Artificial Intelligence


B. Vivekanandam, he is currently employed as the Deputy Dean in the Faculty of Computer Science and Multimedia at Lincoln University College Malaysia. With 15 years of teaching experience and 8 years of research experience, he possesses extensive expertise in these fields. His research interests encompass image processing, face recognition, artificial intelligence, machine learning, and deep learning. He actively serves as a reviewer for esteemed journals such as Elsevier, Inderscience, Wiley, among others. Additionally, he holds a key role as a member of the Curriculum Development team for both direct and ODL (Blended mode) programs, ensuring the quality assurance of Malaysian education.

Author Articles
Mask Region-based Convolution Neural Network (Mask R-CNN) Classification of Alzheimer’s Disease Based on Magnetic Resonance Imaging (MRI)

By Anil Kumar Pallikonda P. Suresh Varma B. Vivekanandam

DOI: https://doi.org/10.5815/ijigsp.2023.06.05, Pub. Date: 8 Dec. 2023

Alzheimer's disease is a progressive neurologic disorder that causes the brain to shrink (atrophy) and brain cells to die. A recent study found that 40 million people worldwide suffer from Alzheimer's disease (AD). A few symptoms of this AD disease are problems with language understanding, mood swings, behavioral issues, and short-term memory loss. A key research area for AD is the classification of stages. In this paper, we applied both binary and multi-class classification. In this paper, proposed is a Mask-Region based Convolution Neural Network (R-CNN) for classifying the stages including MCI, LMCI, EMCI, AD, and CN of Alzheimer's Disease. First performing pre-processing by using the skull-stripping algorithm for removing the noise. Second, the patch wise U-Net has been employed to segment the images for improving the classification process. After that, the system's efficiency is examined using MATLAB-based experiments, utilizing images from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset to evaluate the efficiency in terms of accuracy, precision, recall, specificity, and sensitivity. Our proposed approach to classifying the stages achieves about 98.54%,94.2%, 98.25%, 99.2%, and 99.02%in terms of accuracy with EMCI, CN, MCI, AD, and LMCI respectively. Proposing mask R-CNN with segmentation to classify from CN to AD subjects successfully improved classifier accuracy significantly on the ADNI datasets.

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