Alzheimer’s Stage Classification Using Image Processing

Alzheimer’s Stage Classification Using Image Processing

Apoorva S¹ , Monisha H K² ,Ganashree N³, Harshitha N M⁴, Kavyashree D N⁵

¹ Faculty in Department of Computer Science Engineering, S J C Institute of Technology, Chikkaballapur, India

^2,34,5Department of Computer Science Engineering, S J C Institute of Technology, Chikkaballapur, India

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ABSTRACT: Alzheimer’s disease (AD) is most common type of dementia, is an incurable, progressive neurological brain disease beginning with mild memory loss, possibly leading to loss of the ability to carry on a conversation and respond to environments. It can severely affect the person’s ability to carry out daily activities. Early detection of Alzheimer’s disease can help with proper treatment and prevent brain tissue damage. Several statistical and machine learning models have been exploited for Alzheimer’s disease diagnosis. Analyzing Magnetic Resonance Imaging (MRI) is a common practice for Alzheimer’s disease diagnosis. It can clearly reflect the internal structure of a brain and plays an important role in Alzheimer’s diagnosis of disease. Detection of Alzheimer’s disease is exacting due to the similarity in Alzheimer’s disease MRI data and standard healthy MRI data of older people. Advanced deep learning techniques have successfully demonstrated human level performance in numerous fields including medical image analysis. We have proposed a trained and predictive model using Deep Convolutional Neural Networks (CNN) for Alzheimer’s disease diagnosis using brain MRI data analysis. While most of the existing approaches perform binary classification of disease, our model can identify different stages of Alzheimer’s disease and obtains superior performance for early- stage diagnosis.

KeyWords: Image classification, oversampled, MRI image data, convolution neural network.