Early detection of Alzheimer's disease using local binary pattern and convolutional neural network.

Alzheimer's disease, a progressive and irreversible abnormality of the human brain impairs memory and thinking skills. Gradually, it will damage the ability to carry out simple tasks. Even though the disease cannot be completely cured by medical specialists, the rate of brain damage can be pared if the disease is identified in its budding stage itself. Thus, victims and their relatives will get ample time to prepare themselves. Alzheimer's disease (AD), cognitively normal (CN), mild cognitive impairment convertible (MCIc), and mild cognitive impairment non-convertible (MCInc) are the different phases of cognition. The state of memory loss in aged people, which will not lead to AD, can be encountered as MCInc. The state-MCIc gradually leads to AD. The work is intended for the early detection of AD. Early detection can be claimed if and only if the state-MCIc is detected. But the clinical visual identification of state-MCIc from MRI scan is difficult. In this work, a novel local feature descriptor is proposed for the detection of state-MCIc. The proposed local feature descriptor combined strengths of fast Hessian detector and local binary pattern texture operator for the identification of key points and descriptions. A simple convolutional neural network is used for classification. The classification accuracy between MCIc and CN is obtained as 88.46% which is a pivotal result for early detection of AD. The classification accuracy between AD and CN is attained at 88.99%. The results indicate that the proposed system can contribute a colossal innovation in the early detection of AD. [ABSTRACT FROM AUTHOR]