Automated detection of pulmonary nodules in CT images with support vector machines

2008 ◽  
Author(s):  
Lu Liu ◽  
Wanyu Liu ◽  
Xiaoming Sun
Keyword(s):  
Support Vector Machines ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
Automated Detection ◽  
Support Vector ◽  
Vector Machines

Multi-Class SVMs Based on Priority for Computer-Aided Diagnosis of Lung Nodules in CT Images

2011 ◽  
Vol 291-294 ◽  
pp. 2742-2745
Author(s):  
Qing Zhu Wang ◽  
Xin Zhu Wang ◽  
Ji Song Bie ◽  
Bin Wang
Keyword(s):  
Support Vector Machines ◽  
Sensitivity And Specificity ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Least Square ◽  
Support Vector ◽  
Lung Nodules ◽  
Vector Machines ◽  
Computer Aided ◽  
Aided Diagnosis

A priority based ‘One against all (OAA)’ Multi-class Least Square-Support Vector Machines is designed to remove the unclassifiable regions exist in basic OAA. POAA develops the sensitivity and specificity in Computer-aided Diagnosis (CAD) for detection of lung nodules.

A SVM Based Automated Detection of Uterine Fibroids Using Gabor and Wavelet Features

2012 ◽  
Vol 1 (1) ◽  
pp. 77-85
Author(s):  
N. Sriraam ◽  
D. Nithyashri ◽  
L. Vinodashri ◽  
P. Manoj Niranjan
Keyword(s):  
Support Vector Machines ◽  
Clinical Diagnosis ◽  
Classification Accuracy ◽  
Wavelet Packet ◽  
Uterine Fibroids ◽  
Gabor Filters ◽  
Automated Detection ◽  
Support Vector ◽  
Vector Machines ◽  
Wavelet Features

This paper suggests an automated detection procedure for identification of ultrasonic imaging based uterine fibroids using Gabor filters and wavelet features with support vector machines as classifier. A classification accuracy of 100% is achieved for 86 test images using wavelet packet features, which indicates the potential suitability of the proposed scheme for clinical diagnosis.

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