Multi-level SVM Based CAD Tool for Classifying Structural MRIs
This work provides a CAD tool for doctors to improve diagnosis of neurological disorders, but it is incremental as it builds on existing feature extraction methods with a multi-level classification approach.
The paper tackles the problem of classifying neural lesions from structural MRIs to differentiate Cerebrovascular Accident (CVA) from other disorders, achieving over 86% accuracy using a multi-level SVM system with Non-negative Matrix Factorisation and Haralick features.
The revolutionary developments in the field of supervised machine learning have paved way to the development of CAD tools for assisting doctors in diagnosis. Recently, the former has been employed in the prediction of neurological disorders such as Alzheimer's disease. We propose a CAD (Computer Aided Diagnosis tool for differentiating neural lesions caused by CVA (Cerebrovascular Accident) from the lesions caused by other neural disorders by using Non-negative Matrix Factorisation (NMF) and Haralick features for feature extraction and SVM (Support Vector Machine) for pattern recognition. We also introduce a multi-level classification system that has better classification efficiency, sensitivity and specificity when compared to systems using NMF or Haralick features alone as features for classification. Cross-validation was performed using LOOCV (Leave-One-Out Cross Validation) method and our proposed system has a classification accuracy of over 86%.