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Biomarker to find neurodegenerative diseases using the structural changes in brain using computer vision

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Abstract

The algorithms in Computer vision play a major role in inferring the valuable hidden information from datasets. Huge data analysis requires more concise techniques for analyzing hidden patterns and behavior for correct diagnose. This study addresses the problem of diagnosis of neuro-degenerative diseases like Alzheimer’s disease (AD), Parkinson’s disease (PD) and bipolar disorder (BPD). The potential biomarkers used in this study is extracting structural properties i.e. 3D Speeded Up Robust Feature (SURF) and 3D Scale Invariant Feature Transform (SIFT) features from T1 MRI and extracted volumes of brain tissues. Promising key points are selected by Random Forest and SVM approach to diagnose the type of neurogenerative disease. The classification accuracy is 98.6%. The proposed work revealed exhausted performance when compared to other works.

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Data available on request from the authors.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Dr.Sivanthi Aditanar College of Engineering, Tiruchendur, India

    G. Wiselin Jiji

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  1. G. Wiselin Jiji
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed.

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Correspondence to G. Wiselin Jiji.

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Jiji, G.W. Biomarker to find neurodegenerative diseases using the structural changes in brain using computer vision. Multimed Tools Appl 82, 34981–34993 (2023). https://doi.org/10.1007/s11042-023-14951-8

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  • DOI: https://doi.org/10.1007/s11042-023-14951-8

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