Abstract
Early brain tumor detection has become important to provide timely diagnosis and treatment. Several methodologies are focusing to minimize the manual efforts required for diagnosing by increasing not only the accuracy but also the speed of detection. This proposed methodology includes Otsu’s threshold-based segmentation technique after which feature extraction is done by Grey Level Co-Occurrence Matrix (GLCM) to extract 13 intensities-based and textual-based features. The classification is done through the hybrid model of K-nearest neighbor and Random Forest. The final outcome is generated by majority voting which castes its vote to either one of the above hybrid models. The results are compared to existing algorithms on the basis of performance parameters which includes accuracy, recall, specificity, and execution time.
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Sooch, S.K., Kapoor, N. (2023). Brain Tumor Detection with GLCM Feature Extraction and Hybrid Classification Approach. In: Kumar, R., Verma, A.K., Sharma, T.K., Verma, O.P., Sharma, S. (eds) Soft Computing: Theories and Applications. Lecture Notes in Networks and Systems, vol 627. Springer, Singapore. https://doi.org/10.1007/978-981-19-9858-4_4
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DOI: https://doi.org/10.1007/978-981-19-9858-4_4
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