Deep learning based early Diagnosis of Alzheimer’s disease using Semi Supervised GAN

Abstract

Accurate prediction of Alzheimer’s disease (AD) plays an important role in preventing the memory deficiency and enhancing the quality of life of Alzheimer’s disease patients. For the last decade, neuroimaging was considered as a potential tool for the AD diagnosis. This paper focus on developing a deep learning based end to end model in diagnosing the AD at early stage. The semi supervised Generative Adversarial Network (SSGAN) is designed to automatically classify the presence of AD from Magnetic Resonance Imaging (MRI). By learning from the labeled hippocampus region, a model mapping is built on original image and the segmented result to effectively segments the left and right side hippocampal volume and convolution neural network based feature extraction is applied to extract the deep feature from the segmented area, finally the SSGAN classifier predict the AD. The present study is applied on Alzheimer's disease Neuroimaging Initiative (ADNI) dataset to conduct the experiment. This approach provides the novel deep learning framework for AD detection, which can be utilized for real world patient data to improve the treatment and their quality of life.