A brain tumor prediction system for detecting the tumor disease using mini batch K-Means clustering and CNN

Brain tumor still proves to be one of the major causes of death in the field of cancer. The chances of a person surviving more than 10 years after getting a brain tumor is quite low with different ranges as per age, country and other factors. Prediction of the tumor is a large topic with various algorithms and techniques being used such as imaging methods, machine learning and deep learning models. The models used in the majority of the work doesn't have much comparison with other models to hold the ground and also has insufficient accuracy and other evaluation parameters. Also, the lack of post-processing of the data makes the resultant data unclear and the existence and location of the tumor unclear. Thus, this leads to the need of a system which compare various major models to get the most accurate model with a verification method being applied to it. Moreover, to post process the resultant data for making the resultant image visible clearly to understand the existence of tumor and its location easily. In this work, we propose a new brain tumor prediction system with the incorporation of newly developed segmentation method and CNN for predicting the tumor effectively. The proposed segmentation method applies K-Means clustering algorithm and Mini-Batch K-Means Clustering algorithm for performing effective segmentation process. Here, it performs the segmentation process on the positive tumor datasets that clarifies the existence and location of the tumor. Moreover, the segmented image undergoes 4 major morphological operations such as erosion, dilation, intensity linear transformation and de-noisification to make the resultant image clearly. In addition, the proposed system uses the improved CNN for predicting the tumor disease. Finally, the proposed system is proved as better than the existing classifiers such as Logistic Regression, Support Vector Classifier, K Nearest Neighbour, Random Forest and Decision Tree by conducting experiments with MRI brain images in terms of prediction accuracy, precision, recall, f1-score and time taken for prediction.