Abstract
The identification, detection and classification of brain MRI images into abnormal and healthful is a main pre-clinical step for patients. Standard classification is tedious, valuable, inimitable, and time consuming. Using simple imaging techniques, it is very difficult to have vision about the normal and tumour cell due to the similarities between them. The proposed brain tumour detection method employs ridgelet transform and SVM to identify malignant and benign tumour. In this work, gray level co-occurrence matrix (GLCM) based texture analysis of discrete ridgelet transform coefficients is carried out. SVM classifier is trained using textural features and intensity based features. Principal component analysis (PCA) method is used to lessen the number of features used. SVM outputs the classified image and helps for automated detection. Experimental results demonstrated the efficacy with respect to precision, sensitivity, specificity and accuracy for tumour detection.
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Ankita, P., Subhedar, M. (2019). Pathological Brain Tumour Detection Using Ridgelet Transform and SVM. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1036. Springer, Singapore. https://doi.org/10.1007/978-981-13-9184-2_11
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DOI: https://doi.org/10.1007/978-981-13-9184-2_11
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