Brain Tumor Segmentation and Classification using Java

Brain tumour is a group of tissue that is prearranged by a slow addition of irregular cells. It occurs when cell get abnormal formation within the brain. Recently it is becoming a major cause of death of many people. The seriousness of brain tumor is very big among all the variety of cancers, so to save a life immediate detection and proper treatment to be done. Detection of these cells is a difficult problem, because of the formation of the tumour cells. It is very essential to compare brain tumor from the MRI treatment. Brain tumor is classified into three types: Normal, Benign and Malignant. The neural network will be used to classify the phase of brain tumor that is benign, malignant or normal. Feature extraction by using the Gray Level Co-Occurrence Matrix (GLCM). Image recognition and image compression is done by using the Principal Component Analysis (PCA) method and also large dimensionality of the data is reduced. Automatic brain tumor stage classification is done by using probabilistic neural network (PNN). Segmentation process is done by using K-means clustering algorithm and also detects the brain tumor spread region. Numbers of defect cells are finding in the spreaded region. PNN is fastest technique and also provide the good classification accuracy. Simulation is done by MATLAB 2013 software.