The week's pick
Reseach Article
Classifiers in Context: Prediction of Radiological Characteristic Ratings for Lung Nodule Malignancy
| International Journal of Applied Information Systems |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 5 - Number 2 |
| Year of Publication: 2013 |
| Authors: Vinay. K, Ashok Rao, G. Hemantha Kumar |
10.5120/ijais12-450779
|
Vinay. K, Ashok Rao, G. Hemantha Kumar . Classifiers in Context: Prediction of Radiological Characteristic Ratings for Lung Nodule Malignancy. International Journal of Applied Information Systems. 5, 2 ( January 2013), 14-19. DOI=10.5120/ijais12-450779
Abstract
In this paper, we are exploring a panel of classifier response to an imbalanced medical data set. In this work we are using LIDC (Lung Image Database Consortium) dataset, which is a very good example for imbalanced data. The main objective of this work is to examine how the response of different categories of classifier is, when subjected to imbalanced dataset. We are considering five categories of classifiers which are grouped as, Instance Based classifier, Rule Based classifiers, Functional Classifier, Decision Tree classifier and Ensemble of Classifiers. The results from our experiments will be evaluated based on performance metrics such as Accuracy, Precision, Recall, F-measure, Area under curve and Kappa statistics.
References
- Global cancer statistics, Ahmedin Jemal, Freddie Bray, Melissa M. Center, Jacque Ferlay, Elizabeth Ward, David Forman, A cancer Journal for clinicians. (2008).
- Miles N. Wernick, Yongyi Yang, Jovan G. Brankov, Grigori Yougnaov, Stephen C. Strother. : Machine Learning in Medical Imaging. In: IEEE signal processing Magazine (2010).
- Ekrain Varutbangkul, Vesna Mitrovic, Daniel raichu, Jacob Furst. Combining Boundaries abd Rating from Multiple Observers for Predicting Lung Nodule Characteristics. In: IEEE International Conference on Biocomputing , Bioinformatics and Biomedical technologies, 82-87 (2008).
- Michael C. Lee, Lilla Boroczky, Kivilcim Sungur Stasik, Aaron D. Cann, Alain C. Borczuk, Steve M. Kawut, Charles A. Powell. : Computer-aided diagnosis of Pulmonary nodules using two-step approach for feature selection and classifier ensemble construction In Artificial Intelligence in Medicine, Elsevier 50 (2010) 43-53.
- Katsumi Nakamura,Hiroyuki Yoshida,Roger Engelmann,Heber MacMahon,Shigehiko Katsuragawa, Takayuki Ishida, Kazuto Ashizawa and Kunio Doi,"Computerized Analysis of the Likelihood of Malignancy in Solitary Pulmonary Nodules with Use of Artificial Neural Networks", Radiology 2000; 214:823-830.
- Lee, M. C. ; Boroczky, L. ; Sungur-Stasik, K. ; Cann, A. D. ; Borczuk, A. C. ; Kawut, S. M. ;Powell, C. A. ;Philips Res. North America, Briarcliff Manor, NY "A Two-Step Approach for Feature Selection and Classifier Ensemble Construction in Computer-Aided Diagnosis ", 21st IEEE International Symposium on Computer-Based Medical Systems, 2008.
- Nakumura K, yoshida H, Engelmann R. MacMahon H, Kasturagawa S. Ishida T, et al. computerized analysis of the likliihood of malignancy in solitary pulmonary nodules with use of artifial neural networks. Radiology 823-30. (2000)
- Ebadollahi, S. , Johnson, D. E. . , Diao M, Retrieving clinical cases through a concept space representation of text and images. SPIE Med. Imaging Symp (2008)
- National Cancer Archive website: http://ncia. nci. nih. gov
- Dmitriy Zinovev, Daniela Raicu, Jacob Furst and Samuel G. Armato, "Predicting Radiological Panel Opinions Using Panel of Machine Learning Classifiers", Algorithms 2009, 2, 1473-1502; doi:10. 3390/a2041473.
- Vinay. K, Ashok Rao, Hemantha Kumar. G "Comparative Study on Performance of Single Classifier with Ensemble of Classifiers in Predicting Radiological Experts Ratings on Lung Nodules", Indian International Conference on Artificial Intelligence (IICAI-11). ISBN: 978-0-9727412-8-6, pp 393 – 403
- Vinay. K, Ashok Rao, Hemantha Kumar. G, "Computerized Analysis of Classification of Lung Nodules and Comparison between Homogeneous and Heterogeneous Ensemble of Classifier Model", 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, 978-0-7695-4599-8/11, IEEE DOI 10. 1109/NCVPRIPG. 2011. 56, pp 231-234.
- Timotius, I. K, "Arithmetic means of accuracies: A classifier performance measurement for imbalanced data set", 2010, International Conference onAudio Language and Image Processing (ICALIP), 978-1-4244-5856-1
- David Cieslak and Nitesh Chawla, "Analyzing PETs on Imbalanced Datasets When Training and Testing Class Distributions Differ" PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining, Pages 519-526, ISBN:3-540-68124-8 978-3-540-68124-3
Index Terms
Keywords