Brain Disease Classification Using Deep Learning Technique

Abstract

Alzheimer’s disease (AD) is the most common fatal and progressive neurological disorder that was assumed to result in dementia, learning disabilities and gait inconsistencies. Although the currently existing treatments cannot stop the disorder from becoming more serious, they can temporarily slow the worsening of symptoms and improve the quality of life of the victims and their caregivers, if identified earlier. For the diagnosis of Alzheimer’s, brain Magnetic Resonance Image is normally taken.Deep neural networks provide the efficient result in processing the medical image data compared to the traditional machine learning algorithms. In this paper, the combination of Convolution neural network and deep belief network is introduced for the diagnosis of AD on MRI images. The performance of this automated system is validated by taking samples from Alzheimer’s disease Neuroimaging Initiative. The parameters such as precision, sensitivity, specificity and accuracy are calculated to qualitatively validate the system.