Lung Nodules Classification in CT Images Using Simpson’s Index, Geometrical Measures and One-Class SVM

Automated contouring error detection based on supervised geometric attribute distribution models for radiation therapy: A general strategy

“…In recent years, pattern classification techniques such as support vector machines, 21,22 random forests, 14 and backpropagation neural networks 14,23 have been actively investigated and applied to computer-aided medical diagnosis, 14,24,25 human face recognition, 26,27 and other applications. 24,28,29 These classifier-based approaches typically require balanced (approximately equal number of) positive and negative training samples to derive reliable decision rules for properly separating the sample space.…”

“…For example, Silva and coauthors incorporated geometric features of lung nodules into a support vector machine for differentiating benign or malignant lung nodules. 22 McIntosh and coauthors used a random forest classification technique to estimate delineated contour quality. 14 Even though they took into account neighboring contour constraints with the hope to increase the stability of contour classification, the conditional probability distributions used for the forest inference process in their method required training on a large amount of error samples and was sensitive to the size and asymmetry of the correct and incorrect samples.…”

Informatics and Computerized Tomography Aiding Detection and Diagnosis of Solitary Lung Cancer

“…One-Class SVM was chosen because it was little used in such applications. For more information about this method see (Silva et al, 2009). Fig.…”

Optimal classification for the diagnosis of duchenne muscular dystrophy images using support vector machines

“…Using SVM algorithm, Beom Choi et al [22] successfully distinguished 11 neuromuscular diseases based on microarray data. Additionally, the SVM-based classifiers, such as for pulmonary CT image [23], ovarian ultrasound image [24], small intestine wireless capsule endoscopy (WCE) image [25], liver MRI [26], and brain MRI [27], have been proposed for image classification and disease prediction based on the optimal SVM parameter at a reasonable computational cost parameter [28]. However, no SVM-based classifiers for DMD MRI images have been reported till now to our best knowledge.…”