Brain Tumor Detection and Classification Using Machine Learning Method

Abstract

The technique which is able to process the digital information stored in the form of pixels is called image processing. The medical information from various applications is processed through medical image processing which is a sub-field of image processing. Detecting brain tumors from MRI images is the aim of this research work. Pre-processing, segmentation, feature extraction and classification are the important phases of brain tumor detection technique. For improving the quality of images, the noise will be removed from input image in pre-processing phase. Further, image will be segmented into certain parts through segmentation phase. In the third phase, the approach of features extraction will be applied which extract various features of input image. In the last phase, images will be classified into normal and abnormal images with the help of classification approach. Several machine learning and deep based brain tumor detection techniques have been designed by different researchers over the time. Tumor localization and classification both are the crucial steps while diagnosis the brain tumor. In the existing techniques, tumor segmentation and classification has been done but with high complexity due to which execution time is very high. To overcome this bottleneck and to meet the future needs, there is a need to design a technique which can localize and characterize tumor portion accurately and in least amount of processing time. This will help researchers and professionals from medial organizations to make an optimal choice to use the best technique of tumor detection in their research projects. newline