A Hybrid Siamese-LSTM (Long Short-Term Memory) for Classification of Alzheimer's Disease

Abstract

Alzheimer's disease (AD) is the most general type of dementia, which concludes in memory-related problems. For researchers, the AD diagnosis at an early stage is a complex task. Alzheimer's disease is a chronic neurodegenerative disease. The risk of further degeneration will be decreased by early diagnosis of the disease. Other than predicting the possible growth of the disease, the recent studies concentrate on disease classification state. In this research, a Siamese-LSTM (long short-term memory) model is proposed for the enhanced multiple stages of Alzheimer's disease classification. The BoG bag of visual word method is utilized in order to improve the potency of texture based features (i.e., GLCM). The dataset used in this research work is ADNI. For multi-class classification, the samples are classified into three classes such as normal, AD, and MCI. The performance of the Alzheimer's disease stage is calculated in terms of accuracy, precision, and recall. Moreover, in addition, the sensitivity and specificity are evaluated.