Abstract
The effort of detecting brain tumours by radiologists or clinical experts is arduous and time-consuming, and their accuracy is dependent on their level of knowledge. Medical scans, such as magnetic resonance imaging (MRI), provide a wealth of data that can be exploited to overcome these constraints by creating advanced methodologies and approaches for tumour detection. These approaches can assist radiologists in offering a second opinion when predicting tumours, hence reducing the human aspect in the process. In this context, the paper proposes a hybrid texture-based feature extraction (HTFE) technique by employing Grey level co-occurrence matrix (GLCM) and Gabor Filters for identifying brain tumours. Specially, the proposed HTFE technique assists the classifiers Gradient Boosting (GB), Random Forest (RF), and Decision Tree (DT) in predicting Glioma, Meningioma, and Pituitary brain tumours from T1-weighted contrast-enhanced MRI (T1-CEMRI) dataset. To train and evaluate the classifiers, the HTFE technique extracts a total of seventy-two second order texture features from T1-CEMRI. In terms of accuracy, the suggested HTFE approach beats state-of-the-art techniques.
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Rather, I.H., Minz, S., Kumar, S. (2023). Hybrid Texture-Based Feature Extraction Model for Brain Tumour Classification Using Machine Learning. In: Dutta, P., Bhattacharya, A., Dutta, S., Lai, WC. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1348. Springer, Singapore. https://doi.org/10.1007/978-981-19-4676-9_38
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