Abstract
Deep learning methods have gained more popularity recently in medical image analysis. This work proposes a deep convolutional neural network (DCNN) for Alzheimer’s disease classification using magnetic resonance imaging (MRI) samples. Alzheimer disease (AD) is an irreversible neurological brain disorder; its early symptoms are memory loss and losing thinking abilities called cognitive functions. The accurate diagnosis of Alzheimer’s disease at an early stage is very vital for patient care and conducting future treatment. Deep learning techniques are capable of learning high-level features from dataset compared to hand-crafted feature learning methods such as machine learning techniques. The proposed method classifies the disease as Alzheimer’s disease (AD), mild cognitive impairment (MCI) and normal control (NC). Spyder software obtained from anaconda bundle with Keras library and Tensorflow backend on GPU is used to model DCNN. Experiments are conducted using ADNI dataset and output classification result showed 98.57% accuracy compared to other studies. Our approach also enables us to expand this methodology to predict for more stages of disease classification.
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Horizon Imaging Centre, Surat, Gujarat
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We express our gratitude towards Horizon Imaging Centre for providing us the offline raw dataset for AD.
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Kundaram, S.S., Pathak, K.C. (2021). Deep Learning-Based Alzheimer Disease Detection. In: Nath, V., Mandal, J.K. (eds) Proceedings of the Fourth International Conference on Microelectronics, Computing and Communication Systems. Lecture Notes in Electrical Engineering, vol 673. Springer, Singapore. https://doi.org/10.1007/978-981-15-5546-6_50
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DOI: https://doi.org/10.1007/978-981-15-5546-6_50
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