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• W2895892214 abstract "In this paper, we propose a novel patch based approach for medical image classification. Spherical symmetric 3D local ternary pattern is used to extract the multi-scale multidirectional local features from the sample patches extracted from MRI scans of brain. Artificial neural networks follows this robust feature extraction to learn the features of region of interest (ROI) and labels it into normal or abnormal ROI. Two benchmark medical databases namely: OASIS, BRATS 2012 have been used to analyse the robustness of proposed approach for medical image classification. Performance evaluation of the system has been carried out using recall, precision and F-score. Experimental analysis shows that integration of local features and ANN outperforms the other existing methods for medical image classification." @default.
• W2895892214 created "2018-10-26" @default.
• W2895892214 creator A5037171148 @default.
• W2895892214 creator A5075407864 @default.
• W2895892214 date "2018-03-01" @default.
• W2895892214 modified "2023-09-26" @default.
• W2895892214 title "Learning local features for medical image classification" @default.
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• W2895892214 doi "https://doi.org/10.1109/iceca.2018.8474926" @default.
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