Abstract ▼ AI-Helper

In order to diagnose and prevent Alzheimer's Disease (AD), it is becoming increasingly important to develop a CAD(Computer -aided Diagnosis) system for AD diagnosis, which provides opti mized treatment for patients by analyzing 3D MRI images. It is essential to apply powerful machine learning such a...

In order to diagnose and prevent Alzheimer's Disease (AD), it is becoming increasingly important to develop a CAD(Computer -aided Diagnosis) system for AD diagnosis, which provides opti mized treatment for patients by analyzing 3D MRI images. It is essential to apply powerful machine learning such as deep learn ing in order to automatically classify stages of Alzheimer's Dise ase and to develop a Alzheimer's Disease support diagnosis sys tem that has the function of detecting hippocampus and CSF w hich are important biomarkers in diagnosis of Alzheimer's Disea se. In this paper, we classify MRI cases into three categories of AD, mild cognitive impairment, and normal control according t o MRI image by applying 3D brain MRI image to the Faster R-CNN model and detect hippocampus and CSF in MRI image in real time do. To do this, we use the 2D MRI images extracted from the 3D MRI images of the Faster R-CNN, and perform th e widly used Majority Vote on the resulting bounding box label s. To verify the proposed method, we used the ADNI data set, which is the standard brain MRI database. Experimental results show that the proposed method achieves improved classification performance compared with other state-of-the-art methods.

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