Hippocampus segmentation and classification for dementia analysis using pre-trained neural network models
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Ahana Priyanka
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Kavitha Ganesan
Abstract
The diagnostic and clinical overlap of early mild cognitive impairment (EMCI), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI) and Alzheimer disease (AD) is a vital oncological issue in dementia disorder. This study is designed to examine Whole brain (WB), grey matter (GM) and Hippocampus (HC) morphological variation and identify the prominent biomarkers in MR brain images of demented subjects to understand the severity progression. Curve evolution based on shape constraint is carried out to segment the complex brain structure such as HC and GM. Pre-trained models are used to observe the severity variation in these regions. This work is evaluated on ADNI database. The outcome of the proposed work shows that curve evolution method could segment HC and GM regions with better correlation. Pre-trained models are able to show significant severity difference among WB, GM and HC regions for the considered classes. Further, prominent variation is observed between AD vs. EMCI, AD vs. MCI and AD vs. LMCI in the whole brain, GM and HC. It is concluded that AlexNet model for HC region result in better classification for AD vs. EMCI, AD vs. MCI and AD vs. LMCI with an accuracy of 93, 78.3 and 91% respectively.
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Research funding: Author state no funding involved.
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Authors’ contributions: Ahana Priyanka: Conceptualization, Data Collection, Formal analysis, Validation and Writing original draft. Kavitha Ganesan: Supervision, visualization, Writing – review and editing.
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Conflict of interest: This article declares no conflict of interest.
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Informed consent: This article use public database which include the consent of patient.
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Ethical approval: The data used in this study obtained from public database with proper ethical clearance.
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© 2021 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Review
- Reliability and validity varies among smartphone apps for range of motion measurements of the lower extremity: a systematic review
- Research Articles
- Extended measuring depth dual-wavelength Fourier domain optical coherence tomography
- Linear and non-linear feature extraction from rat electrocorticograms for seizure detection by support vector machine
- Modulation of neo-endothelialization of vascular graft materials by silk fibroin
- Hippocampus segmentation and classification for dementia analysis using pre-trained neural network models
- Modular 3D printable orthodontic measuring apparatus for force and torque measurements of thermoplastic/removable appliances
- Therapeutic maps for a sensor-based evaluation of deep brain stimulation programming
- Voice pathology detection and classification from speech signals and EGG signals based on a multimodal fusion method
Articles in the same Issue
- Frontmatter
- Review
- Reliability and validity varies among smartphone apps for range of motion measurements of the lower extremity: a systematic review
- Research Articles
- Extended measuring depth dual-wavelength Fourier domain optical coherence tomography
- Linear and non-linear feature extraction from rat electrocorticograms for seizure detection by support vector machine
- Modulation of neo-endothelialization of vascular graft materials by silk fibroin
- Hippocampus segmentation and classification for dementia analysis using pre-trained neural network models
- Modular 3D printable orthodontic measuring apparatus for force and torque measurements of thermoplastic/removable appliances
- Therapeutic maps for a sensor-based evaluation of deep brain stimulation programming
- Voice pathology detection and classification from speech signals and EGG signals based on a multimodal fusion method
