Differential evolution based advised SVM for histopathalogical image analysis for skin cancer detection.

Publication Type:
Conference Proceeding
Citation:
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2015, 2015 pp. 781 - 784
Issue Date:
2015-08
Full metadata record
Files in This Item:
Filename Description Size
DFCF60CA-42BA-4741-8A10-80D577E25877 edited.pdfAccepted Manuscript Version487.33 kB
Adobe PDF
Automated detection of cancerous tissue in histopathological images is a big challenge. This work proposed a new pattern recognition method for histopathological image analysis for identification of cancerous tissues. It comprised of feature extraction using a combination of wavelet and intensity based statistical features and autoregressive parameters. Moreover, differential evolution based feature selection is used for dimensionality reduction and an efficient self-advised version of support vector machine is used for evaluation of selected features and for the classification of images. The proposed system is trained and tested using a dataset of 150 histopathological images and showed promising comparative results with an average diagnostic accuracy of 89.1%.
Please use this identifier to cite or link to this item: