Abstract
Brain tumor is revealed due to uncontrolled rapid growth of cells. If not found at an infinity stage, it causes death. Early stage of prediction of diseases of Human’s Brain overcomes a lot of issues. Still classification and segmentation of Brain tumor is very challenging even though having existing promising solutions. Magnetic resonance imaging (MRI) is one forefront technique which is suitable for identifying disease for those who are sufferers. This research focuses on Deep learning techniques for identifying the risk of Brain Tumor and also explores the Brain Tumor detection, segmentation, classification and prediction of severity level of Brain tumor. In this work, MRI The training and testing is done by the CNN from MRI images. The Convolution Neural Network Model is used to classify the images and K-means algorithm is applied to segment the image. Feature extraction applied through Discrete Wavelet Transform and Principal Component Analysis to reduce the dimensional for better accuracy and computational power management. The experiment with deep learning algorithms indicates the effectiveness of the proposed work. A novel method of hybridization of K-Neighboring Algorithm and CNN (K-CNN-BTSL) predicts the severity level of a brain tumor either benign or malignant at an early point with 93% accuracy.
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Saravanan, M., Sellamuthu, S., Bhardwaj, S., Mishr, C., Parthasarathy, R. (2023). A Deep Learning Techniques for Brain Tumor Severity Level (K-CNN-BTSL) Using MRI Images. In: Biswas, A., Semwal, V.B., Singh, D. (eds) Artificial Intelligence for Societal Issues. Intelligent Systems Reference Library, vol 231. Springer, Cham. https://doi.org/10.1007/978-3-031-12419-8_14
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