2018/01/18 by Alexander Khvostikov, Khvostikov, Alexander, Karim Aderghal +10 · 1 citation
Computer Science · Medicine · Neuroscience · Psychology · #68U10 #Advanced Neuroimaging Techniques and Applications #Alzheimer's disease #Artificial intelligence #Brain Tumor Detection and Classification #Cognitive impairment #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Convolutional neural network #Deep learning #Dementia and Cognitive Impairment Research #Diffusion MRI #Disease #FOS: Computer and information sciences #Machine learning #Magnetic resonance imaging #Medical Image Segmentation Techniques #Medicine #Modalities #Modality (human–computer interaction) #Neuroimaging #Neuroscience #Pathology #Pattern recognition (psychology) #Physical medicine and rehabilitation #Positron emission tomography #Psychology #Radiology #cs.CV #msc:68U10
paper · pdf · doi:10.48550/arxiv.1801.05968
arxiv created 2018/01/18 · openalex publication_date 2018/01/18 · arxiv updated 2018/01/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Computer-aided early diagnosis of Alzheimers Disease (AD) and its prodromal\nform, Mild Cognitive Impairment (MCI), has been the subject of extensive\nresearch in recent years. Some recent studies have shown promising results in\nthe AD and MCI determination using structural and functional Magnetic Resonance\nImaging (sMRI, fMRI), Positron Emission Tomography (PET) and Diffusion Tensor\nImaging (DTI) modalities. Furthermore, fusion of imaging modalities in a\nsupervised machine learning framework has shown promising direction of\nresearch.\n In this paper we first review major trends in automatic classification\nmethods such as feature extraction based methods as well as deep learning\napproaches in medical image analysis applied to the field of Alzheimer's\nDisease diagnostics. Then we propose our own algorithm for Alzheimer's Disease\ndiagnostics based on a convolutional neural network and sMRI and DTI modalities\nfusion on hippocampal ROI using data from the Alzheimers Disease Neuroimaging\nInitiative (ADNI) database (http://adni.loni.usc.edu). Comparison with a single\nmodality approach shows promising results. We also propose our own method of\ndata augmentation for balancing classes of different size and analyze the\nimpact of the ROI size on the classification results as well.\n