On the development of a skin cancer computer aided diagnosis system using support vector machine

2019 ◽  
Vol 12 (2) ◽  
pp. 297-308
Author(s):  
Amani Alamri ◽  
Duaa Alsaeed
Keyword(s):  
Support Vector Machine ◽  
Skin Cancer ◽  
Computer Aided Diagnosis ◽  
Support Vector ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Classification of Thyroid Nodules in Ultrasound Images Using Direction-Independent Features Extracted by Two-Threshold Binary Decomposition

2019 ◽  
Vol 18 ◽  
pp. 153303381983074 ◽  
Author(s):  
Antonin Prochazka ◽  
Sumeet Gulati ◽  
Stepan Holinka ◽  
Daniel Smutek
Keyword(s):  
Support Vector Machine ◽  
Ultrasound Imaging ◽  
Random Forests ◽  
Thyroid Nodules ◽  
Computer Aided Diagnosis ◽  
Support Vector ◽  
Computer Aided ◽  
Binary Decomposition ◽  
Independent Features ◽  
Aided Diagnosis

In recent years, several computer-aided diagnosis systems emerged for the diagnosis of thyroid gland disorders using ultrasound imaging. These systems based on machine learning algorithms may offer a second opinion to radiologists by evaluating a malignancy risk of thyroid tissue, thus increasing the overall diagnostic accuracy of ultrasound imaging. Although current computer-aided diagnosis systems exhibit promising results, their use in clinical practice is limited. One of the main limitations is that the majority of them use direction-dependent features. Our intention has been to design a computer-aided diagnosis system, which will use only direction-independent features, that is, it will not be dependent on the orientation and the inclination angle of the ultrasound probe when acquiring the image. We have, therefore, applied histogram analysis and segmentation-based fractal texture analysis algorithm, which calculates direction-independent features only. In our study, 40 thyroid nodules (20 malignant and 20 benign) were used to extract several features, such as histogram parameters, fractal dimension, and mean brightness value in different grayscale bands (obtained by 2-threshold binary decomposition). The features were then used in support vector machine and random forests classifiers to differentiate nodules into malignant and benign classes. Using leave-one-out cross-validation method, the overall accuracy was 92.42% for random forests and 94.64% for support vector machine. Results show that both methods are useful in practice; however, support vector machine provides better results for this application. Proposed computer-aided diagnosis system can provide support to radiologists in their current diagnosis of thyroid nodules, whereby it can optimize the overall accuracy of ultrasound imaging.

scholarly journals Computer–aided diagnosis of diabetes using least square support vector machine

2013 ◽  
Vol 2 (2) ◽  
Author(s):  
Behnaz Naghash Almasi ◽  
Omid Naghash Almasi ◽  
Mina Kavousi ◽  
Amirhossein Sharifinia
Keyword(s):  
Support Vector Machine ◽  
Computer Aided Diagnosis ◽  
Least Square ◽  
Support Vector ◽  
Computer Aided ◽  
Aided Diagnosis
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