“Risk Stratification of Brain lesions in MRI Images using Cascaded Machine Learning Paradigm”

Abstract

Brain lesion is the most severe among all type of lesion, which causes death of a huge number of patients every year. There are two types of brain lesion high-grade glioma andlow-grade glioma (LGG). Amongst both, LGG is fatal for normal life. Early detection of the LGG lesion can save huge amount of life. The article aims to build a cascaded computer aided diagnosis (CAD) system, which can detect the lesion and the severity of the lesion. In the study, a brain MRI dataset is achieved from The Cancer Genome Atlas (TCGA) dataset. Here 3D RGB brain MRI dataset is provided with brain lesion and normal images from 110 patients. Besides this dataset includes the death details of the patient, which is used for severity assessment. At first, the dataset is used to achieve feature for each classification model. Then, two groups of efficient features are selected by Mann-Whitney U test. Those efficient features are used to detect the brain lesion and the severity assessment by two cascaded classification models. Here k-fold, hold out andboth cross validation are applied. In the study, different neural network classifications are applied. Those are from support vector machine, k-nearest neighbour and ensemble classifiers. The results of the classifiers are used for performance evaluation. For the detection of lesion 94.4% accuracy and 95.3% sensitivity are achieved. For risk factor measurement, highest 100% accuracy and sensitivity are achieved..