Affiliations: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
Correspondence:
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Corresponding author: Sanjiban Sekhar Roy, 116-A29, SJT, Vellore Institute of Technology, Vellore, India. Tel.: +91 8754961179; E-mail: sanjibanroy09@gmail.com.
Abstract: Alzheimer’s disease is a brain disorder which causes the malfunction of neurons. This disease can cause loss of brain function and dementia which can further damage memory, thought process and human behaviour. Regardless of being a worst disease, it has no cure. Only handful of classification strategies have been proposed in the literature that too with a small set of training images. Existing methods for the detection of Alzheimer’s disease from MRI images make use of only certain selective subsets of data based on age, gender etc., and often rely on clinical data to aid in their classification. This paper proposes a Convolutional Neural Network (CNN) model for recognition and detection of Alzheimer’s disease from MRI images, trained on the Open Access Series of Imaging Studies (OASIS) dataset. CNNs are the most popular deep learning architectures used for image related problems in recent times. In addition to that, CNNs are also robust for classification, which eliminates the need to ignore certain subsets of data, and solely focus on the image data. The proposed model achieves an accuracy of 80% and can be expected to achieve even higher accuracy with a substantial increase in the amount of data provided for training. We have incorporated Keras library in the python environment for building proposed CNN.
Keywords: Backpropagation, Convolutional Neural Networks, deep learning, OASIS, supervised learning
