2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR) 2014
DOI: 10.1109/socpar.2014.7008037
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Artificial neural network-based classification system for lung nodules on computed tomography scans

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Cited by 69 publications
(61 citation statements)
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“…Several classical machine learning approaches have been previously used for automatic classification of digitised chest images [6,7]. For instance, in [8], three statistical features were calculated from lung texture to discriminate between malignant and benign lung nodules using a support vector machine classifier.…”
Section: Introductionmentioning
confidence: 99%
“…Several classical machine learning approaches have been previously used for automatic classification of digitised chest images [6,7]. For instance, in [8], three statistical features were calculated from lung texture to discriminate between malignant and benign lung nodules using a support vector machine classifier.…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, motivated by the creditable performance of neural network in the fields of computer vision, applying the deep learning technique in medical image has become a main trend that shows promising results. Consequently, various CAD systems based on neural network have been proposed to implement the classification of lung nodules [7][8][9][10][11][12][13][14][15]. Compared to traditional CAD systems, neural network based systems can automatically extract high-level features from the original images by using different network structures.…”
Section: Introductionmentioning
confidence: 99%
“…Krewer et al (2013) proposed a methodology based on a combination of texture and shape features using Correlation-based Feature Subset Selection and KNN, with an accuracy rate of 90.91%. Dandil et al (2014) proposed a methodology based on texture features using Principal Component Analysis (PCA) and Artificial Neural Network (ANN), with an accuracy rate of 90.63%. Parveen and Kavitha (2014) proposed a methodology based on texture features using SVM, with a sensitivity rate of 91.38% and specificity rate of 89.56%.…”
Section: Introductionmentioning
confidence: 99%