Brain Tumor Identification using Dilated U-Net based CNN

Abstract

The identification of brain tumor consumes time and therefore it is important to develop an automated system using an imaging technique. The classification of brain tumor into benign or malignant is performed by using Magnetic Resonance Image (MRI). From the MRI based brain tumor images, the extraction of features is essential for pattern recognition that determines the object based on the color, names, shapes, or more. Therefore, the classifiers are dependent on the strength of features such as shape, color, etc., Yet, the classifiers are dependent on the features that are extracted using deep learning classifiers which are dependent on the features that were extracted. The deep learning algorithm in the medical domain showed interest in the computer vision researchers which consumed time during the process of execution. The proposed Dilated UNet model expands the receptive field for the extraction of multi scale context information. Based on the high resolution conditions, the large scale feature maps and high-resolution conditions are generated using large scale feature maps. It provides rich spatial information that was applied for performing semantic segmentation. Semantic image segmentation is achieved using a U-Net as it adds an expansive path to generate classifications of the pixels belonging to features found in the source image. The existing Kernel based SVM model obtained accuracy of 99.15%, Non-Dominated Sorted Genetic Algorithm-Convolutional Neural Network (NSGA -CNN) obtained accuracy of 99%, Deep Elman Neural network with adaptive fuzzy clustering obtained accuracy of 98%, 3D Context Deep Supervised U-Net obtained accuracy of 92%. Whereas, the proposed Dilated U-Net-based CNN model obtained accuracy of 99.5% better when compared with the existing models.