Learning from imbalanced data: a comparative study for colon CAD

Xiaoyun Yang; Yalin Zheng; Musib Siddique; Gareth Beddoe

doi:10.1117/12.770630

17 March 2008 Learning from imbalanced data: a comparative study for colon CAD

Xiaoyun Yang, Yalin Zheng, Musib Siddique, Gareth Beddoe

Proceedings Volume 6915, Medical Imaging 2008: Computer-Aided Diagnosis; 69150R (2008) https://doi.org/10.1117/12.770630
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

Classification plays an important role in the reduction of false positives in many computer aided detection and diagnosis methods. The difficulty of classifying polyps lies in the variation of possible polyp shapes and sizes and the imbalance between the number of polyp and non-polyp regions available in the training data. CAD schemes for medical applications demand high levels of sensitivity even at the expense of keeping a certain number of false positives. In this paper, we investigate some state-of-the-art solutions to the imbalanced data problem: Synthetic Minority Over-sampling Technique (SMOTE) and weighted Support Vector Machines (SVM). We tested these methods using a diverse database of CT colonography, which included a wide spectrum of dificult cases to detect polyps. We performed several experiments with different combinations of over-sampling techniques on training data. The results demonstrated that SVMs have achieved much better performance over C4.5 with different over-sampling techniques. Also, the results show that weighted SVM without over-sampling can achieve comparable performance in terms of sensitivity and specificity to conventional SVM combined with the over-sampling approach.

Citation Download Citation

Xiaoyun Yang, Yalin Zheng, Musib Siddique, and Gareth Beddoe "Learning from imbalanced data: a comparative study for colon CAD", Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69150R (17 March 2008); https://doi.org/10.1117/12.770630

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available