BRAIN TUMOR CLASSIFICATION USING DEEP LEARNING

Abstract

The early detection of the tumor plays an important role in the recovery of the patient. In our proposed model, we have collected MRI scans as it helps with the information about the blood supply inside the brain. Thus, for the recognition of anomaly, for examining the increasing of the ailment, and for the diagnosis, we prepared a data set consisting of various MRI images. We then focused on removing unwanted noise and image enhancement. The image characteristics can be enhanced by using image preprocessing techniques. The image enhancement depends upon different factors like computational time, computational cost, quality of the uncorrupted image, and the techniques used for noise elimination. We have made use of various filters for the image pre-processing. In our next step, image segmentation, an image is divided into several regions. We have implemented different types of segmentation techniques including active contours snakes, fuzzy C means, and regionderived triple thresholding. We have further implemented two hybrid segmentation models and used computer-aided detection techniques. Post-processing of the data is done using artificial bee colony optimization and watershed filtering and extraction. We then classify two images into tumor and non-tumor category using the VGG-16 CNN model. The features of the segmented images were further classified into various types of tumors, including Glioma tumor, Meningioma tumor, Pituitary tumor, and no tumor using one-hot encoding. This approach was further validated using synthetic and real MR image dataset from Kaggle (name of data set), to detect and classify different types of tumor.