A Robust Machine Learning Approach for Multiclass Alzheimer’s Disease Detection using 3D Brain Magnetic Resonance Images

Abstract

Alzheimer’s disease (AD), a progressive dementia is the neurodegenerative disorder that worsens memory and mental capabilities mostly in aged people. Currently, clinical and psychometric assessments are being used to diagnose the disease in patients. In clinical procedures, 3D Magnetic Resonance Image qualitative parameters are analyzed to identify the abnormality in brain shape, volume, texture, and cortical thickness. This paper presents a robust approach for categorizing 3D MR images into multiple stages of AD using hybrid features viz., Gray Level Co-occurrence Matrix (GLCM), 3D Scale and rotation Invariant Feature Transform (3D SIFT), HOG-TOP and CLBPSM-TOP. The proposed algorithm is validated using Open Access Series of Imaging Studies (OASIS) datasets to classify the subjects into AD, Mild Cognitive Impairment (MCI) and Cognitive Normal (CN) categories using various classifiers. Moreover, this approach is also evaluated and compared with the state of the art techniques. 86.49% diagnosis accuracy is achieved with Ensemble classifier using hybrid features to diagnose the severity of AD. This approach also outperforms majority of these techniques in key parameters viz., accuracy, precision, recall and F1-score.