| Autor: |
Duraisamy B; Hindusthan College of Engineering and Technology, Coimbatore, India. dbaskarphd@gmail.com., Shanmugam JV; Rajagiri School of Engineering and Technology, Cochin, India., Annamalai J; Sri Ramakrishna Institute of Technology, Coimbatore, India. |
| Jazyk: |
angličtina |
| Zdroj: |
Brain imaging and behavior [Brain Imaging Behav] 2019 Feb; Vol. 13 (1), pp. 87-110. |
| DOI: |
10.1007/s11682-018-9831-2 |
| Abstrakt: |
An early intervention of Alzheimer's disease (AD) is highly essential due to the fact that this neuro degenerative disease generates major life-threatening issues, especially memory loss among patients in society. Moreover, categorizing NC (Normal Control), MCI (Mild Cognitive Impairment) and AD early in course allows the patients to experience benefits from new treatments. Therefore, it is important to construct a reliable classification technique to discriminate the patients with or without AD from the bio medical imaging modality. Hence, we developed a novel FCM based Weighted Probabilistic Neural Network (FWPNN) classification algorithm and analyzed the brain images related to structural MRI modality for better discrimination of class labels. Initially our proposed framework begins with brain image normalization stage. In this stage, ROI regions related to Hippo-Campus (HC) and Posterior Cingulate Cortex (PCC) from the brain images are extracted using Automated Anatomical Labeling (AAL) method. Subsequently, nineteen highly relevant AD related features are selected through Multiple-criterion feature selection method. At last, our novel FWPNN classification algorithm is imposed to remove suspicious samples from the training data with an end goal to enhance the classification performance. This newly developed classification algorithm combines both the goodness of supervised and unsupervised learning techniques. The experimental validation is carried out with the ADNI subset and then to the Bordex-3 city dataset. Our proposed classification approach achieves an accuracy of about 98.63%, 95.4%, 96.4% in terms of classification with AD vs NC, MCI vs NC and AD vs MCI. The experimental results suggest that the removal of noisy samples from the training data can enhance the decision generation process of the expert systems. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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