Classification and Diagnosis of Alzheimer's Disease based on a combination of Deep Features and Machine Learning

Abstract

Alzheimer's disease (AD) is a neurodegenerative illness that causes structural changes in the brain over time. Furthermore, various symptoms, most notably memory disorder, relate to these structural alterations in the cerebral cortex. Moreover, capturing these changes in the early stages of the disease visually is a difficult task. As a result, creating an algorithm to detect AD disease will be an enormous accommodation for clinicians. The use of machine learning and deep learning-based approaches requires a huge amount of data, which is not the case with medical images. Therefore, we investigated two transfer learning architectures, the VGG16 and the Inception V3, to extract deep features. Following that, principal component analysis was used to reduce vector dimensionality. Three machine learning algorithms were used to perform classification: the support vector machine, adaBoost, and random forest. The proposed investigations have been tested on the Kaggle and ADNI databases. The highest accuracy rates achieved by the Inception V3 feature, and the Random Forest classifier are for the Kaggle database 73.4% and 77.0% for the ADNI database. The proposed model also outperforms the existing state-of-the-art methods.