Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4572
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dc.contributor.authorPrabhakar, Anarkali-
dc.contributor.authorPranshu Jena, Pranshu Jena-
dc.contributor.authorPati, Umesh C-
dc.date.accessioned2024-05-16T05:38:24Z-
dc.date.available2024-05-16T05:38:24Z-
dc.date.issued2024-05-
dc.identifier.citation2nd International Conference on Smart Systems for application in Electrical Sciences(ICSSES-2024), organized by Siddaganga Institute of Technology, Bangalore, Karnataka, India 3-4 May 2024en_US
dc.identifier.urihttp://hdl.handle.net/2080/4572-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractTexture-based feature extraction is crucial in brain tumor feature extraction and classification. The texture-based feature extraction provides valuable information on the textural difference between the tumorous and non-tumorous region and its spatial arrangement of pixel intensities. In this proposed framework, machine learning models using Gray-Level Co-occurrence Matrices (GLCM) and Gabor filter on localized global feature patches have been developed to classify different types of brain tumors. The best results were obtained using GLCM features: energy, contrast, correlation, and homogeneity. The concatenation of the features of localized 35×35 patches reduces the computational requirements and complexity. The proposed framework provides robust methods for characterizing various texture-based features using localization. Figshare, a publicly available dataset of 3064 images, has been used in this work for three classes: Meningioma, GLioma, and Pituitary. The no-tumor dataset has been obtained by publicly available Br35H, making 4010 images. The dataset is divided into 70:30, 80:20, and 90:10. The best result is established with the split of 70:30 in comparison to 80:20 and 90:10. The features are selected using the Atrous Convolution Autoencoder (A-CAE). The dimensions have been reduced by 42%. The selected features are then provided as input to the Support Vector Classifier (SVC), Random Forest (RF), K-Nearest Neighbour (KNN), and Extreme Gradient Boost(XGBoost). Out of which, the XGBoost classifier on hyperparameter tuning has obtained the highest accuracy of 91.83%.en_US
dc.subjectBrain tumoren_US
dc.subjectFeature Extractionen_US
dc.subjectGlobalen_US
dc.subjectLocaliseden_US
dc.subjectAtrous Convolution Autoencoder (A-CAE)en_US
dc.titleA New Framework for Brain Tumor Feature Extraction and Classification Using Localized Global Feature Patchesen_US
dc.typeArticleen_US
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