Radiomic Features Based Severity Prediction in Dementia MR Images Using Hybrid SSA-PSO Optimizer and Multi-class SVM Classifier

• Published in: Ingénierie et recherche biomédicale Vol. 43; no. 6; pp. 549 - 560
• Main Authors: P, Ahana, G, Kavitha
• Format: Journal Article
• Language: English
• Published: Elsevier Masson SAS 01.12.2022
• Subjects: Biomarker; Dementia; Internal Medicine; Optimizer; Progression; SSA-PSO; Progression; SSA-PSO; Biomarker; Optimizer; Dementia
• ISSN: 1959-0318
• DOI: 10.1016/j.irbm.2022.05.003

Objectives: In recent times, MR image is used to detect the dementia diagnostic differences in preclinical stages. Mild cognitive impairment (MCI) is characterized by slight cognitive deficits. This can be categorized into early and late mild cognitive impairment according to extent of episodic cognitive impairment. There is a higher risk of MCI subject to convert into Alzheimers disease. It is observed that there is no appropriate biomarker to find severity changes in dementia. Thus, this work aims to identify appropriate biomarker using radiomic and hybrid social algorithms. Materials: ADNI database is utilized for this study. Grey matter, cerebrospinal fluid, ventricle, hippocampus, brain stem and mid brain regions are examined to extract the radiomic features. This provides local and global tissue changes of these regions. The significant features are obtained using hybrid salp swarm and particle swarm optimization method (SSA-PSO). SVM is adopted to classify the normal and severity groups. The performance of work is validated clinically and statistically. Results: Results show that radiomic features capture anatomical changes for considered regions. The significant features from SSA-PSO show greater causal association and statistical significance for all considered regions. However, hippocampus achieves 88.5% of classification accuracy than other regions in the considered group. The inter class variations of hippocampus gives precise prognosis differences. From the clinical validation, it is also found that the obtained result show high statistical significance (p<0.0001) among the different severity. Conclusion: The proposed work shows promising results in using these biomarkers in detection of dementia and support clinical decisions. •Various brain biomarkers changes are analyzed for dementia differential diagnosis.•Radiomic features significantly capture shape, texture and pattern variations.•Novel hybrid SSA-PSO optimizer is used to for selection of significant feature set.•Entire framework is statically and clinically validated for large image samples.•Hippocampus shows significant variation to discriminate normal and severity stages.