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Deep Learning Techniques for Prediction, Detection, and Segmentation of Brain Tumors
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Author(s): Prisilla Jayanthi (K. G. Reddy College of Engineering and Technology, India)and Muralikrishna Iyyanki (Defence Research and Development Organisation, India)
Copyright: 2020
Pages: 37
Source title:
Deep Neural Networks for Multimodal Imaging and Biomedical Applications
Source Author(s)/Editor(s): Annamalai Suresh (Department of Computer Science and Engineering, Nehru Institute of Engineering and Technology, Coimbatore, India), R. Udendhran (Department of Computer Science and Engineering, Bharathidasan University, India)and S. Vimal (Department of Information Technology, National Engineering College (Autonomous), Kovilpatti, India)
DOI: 10.4018/978-1-7998-3591-2.ch009
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Abstract
In deep learning, the main techniques of neural networks, namely artificial neural network, convolutional neural network, recurrent neural network, and deep neural networks, are found to be very effective for medical data analyses. In this chapter, application of the techniques, viz., ANN, CNN, DNN, for detection of tumors in numerical and image data of brain tumor is presented. First, the case of ANN application is discussed for the prediction of the brain tumor for which the disease symptoms data in numerical form is the input. ANN modelling was implemented for classification of human ethnicity. Next the detection of the tumors from images is discussed for which CNN and DNN techniques are implemented. Other techniques discussed in this study are HSV color space, watershed segmentation and morphological operation, fuzzy entropy level set, which are used for segmenting tumor in brain tumor images. The FCN-8 and FCN-16 models are used to produce a semantic segmentation on the various images. In general terms, the techniques of deep learning detected the tumors by training image dataset.
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