Alzheimer’s diseases classification using YOLOv2 object detection technique

Early diagnosis and treatment of Alzheimer's disease (AD) is necessary for the patient safety. Computer-aided (CAD) a useful tool early (AD). We make two contributions to solution this problem in study. To begin with, we are first propose an based on MATLAB that does not require any magnetic resonance imaging (MRI) pre-processing. Second, apply recent deep learning object detection architectures like YOLOv2 disease. A new reference data set containing 300 raw points detection/normal control severe stage (MCI/AD/NC) presented. Primary screening cases each category from neuroimaging initiative (ADNI) dataset. The T1-weighted Dicom MRI slice MP-Rage series 32-bit DICOM image format PNG included By using MATLAB’s label tool, test were marked with their appropriate class bounding box. It was possible achieve accuracy 0.98 trial without usage preprocessing technology.