16 June 2017 Classification of breast masses in ultrasound images using self-adaptive differential evolution extreme learning machine and rough set feature selection
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Abstract
A method using rough set feature selection and extreme learning machine (ELM) whose learning strategy and hidden node parameters are optimized by self-adaptive differential evolution (SaDE) algorithm for classification of breast masses is investigated. A pathologically proven database of 140 breast ultrasound images, including 80 benign and 60 malignant, is used for this study. A fast nonlocal means algorithm is applied for speckle noise removal, and multiresolution analysis of undecimated discrete wavelet transform is used for accurate segmentation of breast lesions. A total of 34 features, including 29 textural and five morphological, are applied to a k -fold cross-validation scheme, in which more relevant features are selected by quick-reduct algorithm, and the breast masses are discriminated into benign or malignant using SaDE-ELM classifier. The diagnosis accuracy of the system is assessed using parameters, such as accuracy (Ac), sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), Matthew’s correlation coefficient (MCC), and area ( A z ) under receiver operating characteristics curve. The performance of the proposed system is also compared with other classifiers, such as support vector machine and ELM. The results indicated that the proposed SaDE algorithm has superior performance with Ac = 0.9714 , Se = 0.9667 , Sp = 0.975 , PPV = 0.9666 , NPV = 0.975 , MCC = 0.9417 , and A z = 0.9604 compared to other classifiers.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Kadayanallur Mahadevan Prabusankarlal, Palanisamy Thirumoorthy, Radhakrishnan Manavalan, "Classification of breast masses in ultrasound images using self-adaptive differential evolution extreme learning machine and rough set feature selection," Journal of Medical Imaging 4(2), 024507 (16 June 2017). https://doi.org/10.1117/1.JMI.4.2.024507 . Submission: Received: 1 November 2016; Accepted: 25 May 2017
Received: 1 November 2016; Accepted: 25 May 2017; Published: 16 June 2017
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