Brain tumors are very hazardous for a patient whether its malignant or benign syndrome, which drags to a minutely minuscular life cycle in the highest degree. So the, treatment is very consequential way to boost up the life of expectancy. More than one convolution layers with deep neural network is utilized for finding feature in neoplasm image. The utilization of diminutive kernels (3*3 or 5*5 size) sanctions designing a deeper design, besides having a positive impact against over fitting. The goal is classification with segmentation of tumor part with the help of convolutional neural network and Watershed Algorithm. In this paper the input to the system is considered as brain scanned MRI image. CNN will classifies the image for presence of tumor and if tumor is present then it will be processed by watershed segmentation (MARKER BASED) and morphological operation. Area calculation of tumor is also done within process. Experimental results show that the CNN archives rate of 98 % accuracy with low complexity.
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Classification of Brain Tumor Using Convolutional Neural Network
Semantic Scholar · Computer Science · 2019
Abstract
Brain tumors are very hazardous for a patient whether its malignant or benign syndrome, which drags to a minutely minuscular life cycle in the highest degree. So the, treatment is very consequential way to boost up the life of expectancy. More than one convolution layers with deep neural network is utilized for finding feature in neoplasm image. The utilization of diminutive kernels (3*3 or 5*5 size) sanctions designing a deeper design, besides having a positive impact against over fitting. The goal is classification with segmentation of tumor part with the help of convolutional neural network and Watershed Algorithm. In this paper the input to the system is considered as brain scanned MRI image. CNN will classifies the image for presence of tumor and if tumor is present then it will be processed by watershed segmentation (MARKER BASED) and morphological operation. Area calculation of tumor is also done within process. Experimental results show that the CNN archives rate of 98 % accuracy with low complexity.