Classification of Alzheimer's Disease MRI Images with CNN Based Hybrid Method

Citations Over TimeTop 10% of 2020 papers

Abstract

Alzheimer is a type of dementia disease that is common in older ages. This disease is a progressive form of neurological disease that causes the destruction of brain cells. Since Alzheimer's is a progressive disease, various problems increase over time. For this reason, it is very important to diagnose the disease early and start the treatment process. In this study, it was tried to determine at which stage the disease is or whether it is Alzheimer using brain images. CNN architectures are used to diagnose the disease. In addition, a hybrid method we have developed has been proposed. With the architectures used, it is classified in 4 stages according to the disease progression level. In the proposed hybrid model, the Resnet50 method is used as the basis. The results are obtained separately by Alexnet, Resnet50, Densenet201, Vgg16, and the Hybrid method we developed. An accuracy of 90% has been achieved with the developed hybrid model. Consequently, when other scientific paper in the literature are investigated, it is finalized that the hybrid model developed to diagnose Alzheimer's disease has achieved the success achieved by other CNN architectures and even offers better results.

Related Papers

• → An Object Detection and Pose Estimation Approach for Position Based Visual Servoing(2017)5 cited
• → Self-monitoring to improve robustness of 3D object tracking for robotics(2011)4 cited
• → Object-oriented stripe structured-light vision-guided robot(2017)2 cited
• → 3-D tracking of a moving object by an active stereo vision system(2002)9 cited
• → Robust object tracking based on RGB-D camera(2014)