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A survey on Brain Tumor Detection using Machine Learning

Early detection of a tumour when it is tiny, lowers the impact of surgery and therapy, improving the prognosis for many patients. For the detection of tumors, MRI images at various stages from a number of patients can be used. The availability of the variety in extraction of feature and classification methods from MRI help in detection of tumors in the brain for amelioration. For detection of tumors at an early stage, use of Convolutional Neural Network image classification algorithm would help in predicting with high accuracy. In the survey, we investigate the efficacy of using 3D CNNs for brain tumor detection. Production of 3D CNNs is still at an nascent phase due to the intricacy, the gains might lead to automatic detection of important features without supervision. Being computationally very efficient, makes it the widely used architecture. Among the survey papers, a 3D CNN architecture is designed to exact tumors and the extracted tumors, for feature extraction, are passed to a pre-trained CNN model. As the output, these features are transferred to the correlation-based selection method among which the best features are selected. Validation is done through feed-forward neural networks for final classification. Limitations of these networks consist of the high expense of computation and the number of parameters increasing in an exponential manner. As the highest accuracy is found with the use of pipelining of RBF kernel, and polynomial kernel classification, scope of development exists in ensemble techniques that can be used.