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

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.

Automated summary

Algorithms in computer vision are crucial for extracting valuable hidden information from datasets. This study focuses on diagnosing neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and bipolar disorder. It uses potential biomarkers extracted from T1 MRI and brain tissue volumes, specifically the 3D Speeded Up Robust Feature (SURF) and 3D Scale Invariant Feature Transform (SIFT) features. Random Forest and SVM approaches are employed to select key points for diagnosis, achieving a classification accuracy of 98.6%.