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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Classification of Brain Tumor Using Convolutional Neural Network




    Publication date :

    2019-06-01


    Size :

    2332381 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Classification of Brain Tumor based on Convolution Neural Network

    Srivastava, Mayank / Mishra, Adarsh / Dixit, Pratibha | IEEE | 2024




    Brain Tumor Segmentation and Classification using Deep neural networks

    Alagarsamy, Saravanan / Durgadevi, V. / Shahina, A. et al. | IEEE | 2024


    Vehicle Type Classification Using a Semisupervised Convolutional Neural Network

    Dong, Zhen / Wu, Yuwei / Pei, Mingtao et al. | IEEE | 2015