title: Differential evolution based advised SVM for histopathalogical image analysis for skin cancer detection.
abstract: 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%.
authors.forename: Ammara
authors.initials: A
authors.lastname: Masood
authors.forename: Adel
authors.initials: A
authors.lastname: Al-Jumaily
mesh_terms: D000465:Algorithms; D006801:Humans; D010363:Pattern Recognition, Automated; D012867:Skin; D012878:Skin Neoplasms; D060388:Support Vector Machine