A Review Paper on Brain Tumor Detection and Classification
Keywords:
Neural Networks, Magnetic resonance imaging, Data Wavelet TransformsAbstract
nowadays, there is a great rise in disease called brain tumor. Now with such an enormous growth in it has become very important to make use of computer to make use of computer-based detection software to detect brain tumor and its types efficiently in less time. Nowadays magnetic resonance imaging (MRI) images. Is playing a very important role for detecting the types of tumors. From previous analysis it is being observed that Convolutional Neural Network (CNN) is been designed for the overall accuracy purpose with 97.5% accuracy. Likewise, accuracy is high and validation loss is very low [1]. The following contains five sections: introduction, methodology, literature review, and conclusion. In the study, we have discussed numerous sorts of approaches for identifying brain tumors.
References
J. Seetha and S. . S. Raja, "Brain Tumor Classification Using Convolutional Neural Networks.," Biomedical & Pharmacology Journal, vol. 11, no. 3, p. 1457, 2018.
H. Mohsen, E.-S. . A. El-Dahshan, E.-S. M. El-Horbaty and A.-B. M. Salem, "Classification using deep learning neural networks for brain tumors," Future Computing and Informatics Journal, vol. 3, no. 1, pp. 68-71, 2018.
M. Sharma, P. Sharma, R. Mittal and K. Gupta, "Brain tumour detection using machine learning," Journal of Electronics, vol. 3, no. 4, pp. 298-308, 2021.
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