Abstract
The unexpected growth of nerves inside the human brain that interferes with the normal function of the brain is referred to as a brain tumor. Magnetic Resonance Imaging (MRI) is used to provide images of better resolution of the brain. This paper proposes a system that applies a compound filter, along with a Convolution Neural Network (CNN) and Support Vector Machine (SVM) for the detection of brain tumors in MRI. For finding tumors this proposed system has been divided into the following sections: preprocessing, segmentation, feature extraction and tumor detection. This system employs a compound filter for preprocessing that is made up of Gaussian, mean and median filters. Threshold and histogram-based techniques have been applied for image segmentation and Grey Level Co-occurrence Matrix (GLCM) for feature extraction. For tumor detection, the SVM and CNN classifiers were employed. CNN is a Deep Neural Network (DNN) based classifier. The tumor detection accuracy of CNN and SVM classifiers have been estimated at 98.06% and 93.28%, respectively. The proposed system concludes that the accuracy of CNN is superior to SVM.
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Ramtekkar, P.K., Pandey, A., Pawar, M.K. (2023). Accurate Detection of Brain Tumor Using Compound Filter and Deep Neural Network. In: Salem, M., Merelo, J.J., Siarry, P., Bachir Bouiadjra, R., Debakla, M., Debbat, F. (eds) Artificial Intelligence: Theories and Applications. ICAITA 2022. Communications in Computer and Information Science, vol 1769. Springer, Cham. https://doi.org/10.1007/978-3-031-28540-0_8
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