To extract the high-level features such as edges from the input image and also classify the input image is a Normal image or Abnormal (Tumor).The model is implemented using MATLAB using Convolution Neural Network (CNN) algorithm for Brain Tumor Segmentation
To extract the high- level features such as edges from the input image.
Classify the input image obtain an output image as normal or abnormal (Tumor)
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[1]D. Daimary, M. Bora, K. Amitab and D. Kandar, "Brain Tumor Segmentation from MRI Images using Hybrid Convolutional Neural Networks", Procedia Computer Science, vol. 167, pp. 2419-2428, 2020. Available: 10.1016/j.procs.2020.03.295. [2]D. Daimary, M. Bora, K. Amitab and D. Kandar, "Brain Tumor Segmentation from MRI Images using Hybrid Convolutional Neural Networks", Procedia Computer Science, vol. 167, pp. 2419-2428, 2020. Available: 10.1016/j.procs.2020.03.295. [3]M. Malathi and P. Sinthia, "Brain Tumour Segmentation Using Convolutional Neural Network with Tensor Flow", Asian Pacific Journal of Cancer Prevention, vol. 20, no. 7, pp. 2095-2101, 2019. Available: 10.31557/apjcp.2019.20.7.2095. [4]D. Daimary, M. Bora, K. Amitab and D. Kandar, "Brain Tumor Segmentation from MRI Images using Hybrid Convolutional Neural Networks", Procedia Computer Science, vol. 167, pp. 2419-2428, 2020. Available: 10.1016/j.procs.2020.03.295. [5]G. Tabatabai et al., “Molecular diagnostics of gliomas: The clinical perspective,” Acta Neuropathology, vol. 120, no. 5, pp. 585–592, 2010.