An intelligent technique for detecting Alzheimer's disease based on brain structural changes and hippocampal shape

In this work, we present a supervised learning framework to help early diagnosis of Alzheimer's disease (AD) from structural magnetic resonance imaging. The state of art of this work is based on the hippocampus shape and structure of the brain components. This framework works in three phases. In the first phase, extraction of shape and texture features from the image. In the second phase, hippocampus detection and its volume calculation are performed. In the third phase, the features were used to classify the groups using discriminant function analysis. We use support vector machine to classify into groups and also compare with two earlier works. The results proved that the proposed architecture has high contribute to computer-aided diagnosis of AD. Our empirical evaluation has a superior retrieval and diagnosis performance when compared with the performance of other works.

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