Abstract
The early detection of brain tumor can drastically improve the survival rate of patients. The MRI images of brain tumor Meningioma and Glioma are used for classification. With the help of Gray-Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT), different features are extracted from the image. Then, the tumor segmentation is done to discover which part of the tumor area is affected after the detection of tumor. Tumor classification is done using CNN (Convolution Neural Networks). The effects could be pretty helpful for the specialists and radiologists for early detection and if the classifier does not identify any tumor, then it concludes that there is no tumor, if it locates any type of tumor, then we are able to find out the location affected also. Accuracy, sensitivity, and specificity were used to evaluate the proposed approach. A GUI (Graphical User Interface) has been created for the usage of the MATLAB 2013a.
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Bhargavi, K., Mani, J.J.S. (2019). Early Detection of Brain Tumor and Classification of MRI Images Using Convolution Neural Networks. In: Saini, H., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 74. Springer, Singapore. https://doi.org/10.1007/978-981-13-7082-3_49
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DOI: https://doi.org/10.1007/978-981-13-7082-3_49
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