Cataloging of Happy Facial Affect Using a Radial Basis Function Neural Network

Nachamai, M; Dutta, Pranti

doi:10.1007/978-81-322-0997-3_53

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2020

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“…As the considered research problem in this thesis is of complex in nature, the developed enhanced GSO is employed to compute the optimal feature subsets and as well to train the RBFNN used for brain tumor classification [11][12][13][14][15], [17][18].…”

Section: End End End Stopmentioning

confidence: 99%

Brain Tumor Classification by EGSO Based RBFNN Classifier

Sharma R*,

Sungheetha,

Nuradis

2020

IJRTE

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Tumor classifier is modelled employing a proposed Enhanced Group Search Optimizer based Radial Basis Function Neural Network model is applied in this research contribution to acquire the ideal instances from the developed VOI instance an as well EGSO is utilized to optimize the weight values of the Radial Basis Function Network classifier by limiting the mean square mistake. The anticipated EGSO based RBFNN classifier brings better characterization precision and accomplished insignificant error with quicker process. The simulation results computed prove the effectiveness of the RBFNN classifier to be better in comparison with the other proposed classifiers in this thesis and that available in the literature. The proposed pattern evaluation technique presents an automatic cancer categorization procedure thru the ultimate facets which fantastic characterizes MRI brain image is benign and malignant cancers. The planned method may perhaps stretch to categorize exceptional classes of tumor (eg. Meningioma, glioma etc.,) and depth of malignancy.

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