A medical decision making system for brain tumor identification from magnetic resonance images using machine learning techniques

Abstract

Brain tumor is an abnormal and uncontrolled growth of the cells. Early brain tumor detection is essential to save lives. In fact, brain tumors are difficult to diagnose, requiring specialized equipment and training. A medical decision making system facilitates diagnostic process by visualizing the data produced by a classification system, allowing doctors to make a right diagnosis. This study proposes an automated system for segmentation and classification the brain tumor grades in MRI into three classes: normal, LGG and HGG. In the proposed system, a new segmentation method named LDI-Means algorithm (Local Difference in Intensity-Means algorithm) is used. It is a clustering technique based on the difference in the intensity level of one pixel than another. Furthermore, a new approach in selecting the sub-significant set of attributes is used, denoted MI+SVD (Mutual Information + Singular Value Decomposition). The robust features are later used as an input to the classifier. The new network structure called simplified RNN (Residual Neural Network) is also offered by this study. The proposed automated system has six stages; the pre-processing, clustering by LDI-Means, feature extraction, feature selection and dimension reduction by MI+SVD, and classification by simplified RNN. The experimental findings at the end of the segmentation stage presented an approximate match of 99.02% with the hand-labeled images. In addition, in comparison to the original feature space and two standard dimension reduction xiv methods, PCA and SVD, the MI+SVD algorithm offered a more efficient result for improving the classification process to achieve a satisfied grading of brain tumors. Furthermore, using a simplified RNN as a classifier provides a high level of effectiveness to the proposed system. In comparison with other published studies, it is found that the proposed system is very sufficient to offer a meaningful real-time estimation for identification the brain tumor grades.