D3mciAD : Data-Driven Diagnosis of Mild Cognitive Impairment Utilizing Syntactic Images Generation and Neural Nets

Alzheimer’s disease, an incurable chronic neurological disorder (NLD) that affects human memory and demises cognitive thinking ability with shrinkage of the brain area. Early detection of Alzheimer’s disease (AD) is the only hope to delay its effect. This study designed a computer-aided automated detection method that can detect mild cognitive impairment for AD from magnetic resonance image scans. The data-driven solution approach requires an extensive quantity of annotated images for diagnosis. However, obtaining a large amount of annotated data for medical application is a challenging task. We have exploited a deep convolutional generative adversarial network (DCGAN) for synthesizing high-quality images to increase dataset size. A fine-tuned CNN (VGG16 architecture) model works on images to extract the intuitive features for early diagnosis. The extracted features of images by VGG16 feed into the support vector machine for classification. This research has conducted copious experiments to validate the proposed method outperformed relative baselines on public datasets. ISBN för värdpublikation: 978-3-030-86992-2; 978-3-030-86993-9