Brain tumor is the result of an abnormal growth of cells, which reproduce themselves in an uncontrolled manner. This type of tumour is diagnosed through Magnetic Resonance Imaging (MRI), which plays a significant role in segmenting the tumor region into different ways for performing surgical and medical planning assessment but the manual detection may lead to errors and it is a time consuming process. To overcome the problem, experts use various algorithms for automatic detection of the tumor region, which are based on deep learning algorithms. They are designed to train and tune millions of images within a short period of time. Further, this paper proposes different types of classification methods with a number of iterations are based on CNN architectures such as VggNet, GoogleNet and ResNet 50. For 60 iterations VggNet reports 89.33% accuracy, GoogleNet 93.45% and ResNet 50 96.50%. Finally, it is proved that ResNet 50 achieves better results than VggNet and GoogleNet with comparatively less time and better accuracy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Automatic Detection of Brain Tumor Using Deep Learning Algorithms


    Contributors:


    Publication date :

    2020-11-05


    Size :

    89146 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Automatic Question Tagging using Machine Learning and Deep learning Algorithms

    Prajapati, Mihir / Nakrani, Mitul / Vyas, Tarjni et al. | IEEE | 2022



    Smart Pothole Detection System using Deep Learning Algorithms

    Chougule, Savita / Barhatte, Alka | Springer Verlag | 2023


    Training Deep Learning Spacecraft Component Detection Algorithms Using Synthetic Image Data

    Viggh, Herbert / Loughran, Sean / Rachlin, Yaron et al. | IEEE | 2023


    Predicting Pavement Roughness Using Deep Learning Algorithms

    Zhou, Qingwen / Okte, Egemen / Al-Qadi, Imad L. | Transportation Research Record | 2021