Abstract
The development of brain cancer treatment is fully dependent on physician’s and radiologist’s knowledge. An automated classification method can improve the physician’s knowledge to accelerate the treatment process. This paper aims at developing an artificial neural network (ANN) based automated brain tumor classifier with a hybrid feature extraction scheme. The classification approach is started with the preprocessing of the tumor images using the min-max normalization rule. Then the statistical features of the preprocessed images are extracted utilizing the hybrid feature extraction method comprised of stationary wavelet transform (SWT) and Gray level co-occurrence matrix (GLCM) techniques to enhance the classification performance. Finally, the ANN is employed for classifying the brain tumors in most frequent brain tumor types as glioma, meningioma, and pituitary. The proposed approach provides an improved classification accuracy of 96.2% that is much better than modern multi-classification methods because of employing an SWT based hybrid feature extraction method rather than the conventional discrete wavelet transform (DWT) technique.
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Sathi, K.A., Saiful Islam, M. (2021). Multi-classification of Brain Tumor Images Based on Hybrid Feature Extraction Method. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_83
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DOI: https://doi.org/10.1007/978-3-030-68154-8_83
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