Research Area:  Machine Learning

Abstract:

The origin of dementia can be largely attributed to Alzheimers disease (AD). The progressive nature of AD causes the brain cell deterioration that eventfully leads to physical dependency and mental disability which hinders a persons normal life. A computer-aided diagnostic system is required that can aid physicians in diagnosing AD in real-time. The AD stages classification remains an important research area. To extract the deep-features, the traditional machine learning-based and deep learning-based methods often require large dataset and that leads to class imbalance and overfitting issues. To overcome this problem, the use an efficient transfer learning architecture to extract deep features which are further used for AD stage classification. In this study, an Alzheimers stage detection system is proposed based on deep features using a pre-trained AlexNet model, by transferring the initial layers from pre-trained AlexNet model and extract the deep features from the Convolutional Neural Network (CNN). For the classification of extracted deep-features, we have used the widely used machine learning algorithms including support vector machine (SVM), k-nearest neighbor (KNN), and Random Forest (RF). The evaluation results of the proposed scheme show that a deep feature-based model outperformed handcrafted and deep learning method with 99.21% accuracy. The proposed model also outperforms existing state-of-the-art methods.

Keywords:  
Alzheimer stage detection
Dementia
CNN
AlexNet
Deep features

Author(s) Name:  Hina Nawaz, Muazzam Maqsood, Sitara Afzal, Farhan Aadil, Irfan Mehmood & Seungmin Rho

Publisher name:  Springer

DOI:  10.1007/s11042-020-09087-y

Volume Information:  80, pages 35789–35807 (2021)

Paper Link:   https://link.springer.com/article/10.1007/s11042-020-09087-y