Automated multi-class brain tumor types detection by extracting RICA based features and employing machine learning techniques.

Among the other cancer types, the brain tumor is one the leading cause of cancer across globe. If the tumor is properly identified at an earlier stage, then the chances of the survival can be increased. To categorize the brain tumor there are several factors including texture, type and location of brain tumor. We proposed a novel reconstruction independent component analysis (RICA) feature extraction method to detect multi-class brain tumor types (pituitary, meningioma, and glioma). We then employed the robust machine learning techniques as support vector machine (SVM) with quadratic and linear kernels and linear discriminant analysis (LDA). For training and testing of the data validation, a 10-fold cross validation was employed. For the multi-class classification, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and AUC were, respectively, 97.78%, 100%, 100%, 99.07, 99.34% and 0.9892 to detect pituitary using SVM Cubic followed by meningioma with accuracy (96.96%0, AUC (0.9348) and glioma with accuracy (95.88%), AUC (0.9635). The findings indicates that RICA feature based proposed methodology has more potential to detect the multiclass brain tumor types for improving diagnostic efficiency and can further improve the prediction accuracy to achieve the clinical outcomes.