These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
242 related articles for article (PubMed ID: 23727300)
1. Comparative evaluation of support vector machines for computer aided diagnosis of lung cancer in CT based on a multi-dimensional data set. Sun T; Wang J; Li X; Lv P; Liu F; Luo Y; Gao Q; Zhu H; Guo X Comput Methods Programs Biomed; 2013 Aug; 111(2):519-24. PubMed ID: 23727300 [TBL] [Abstract][Full Text] [Related]
2. A Novel Hybrid Feature Extraction Model for Classification on Pulmonary Nodules. Kailasam SP; Sathik MM Asian Pac J Cancer Prev; 2019 Feb; 20(2):457-468. PubMed ID: 30803208 [TBL] [Abstract][Full Text] [Related]
3. Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization. Nishio M; Nishizawa M; Sugiyama O; Kojima R; Yakami M; Kuroda T; Togashi K PLoS One; 2018; 13(4):e0195875. PubMed ID: 29672639 [TBL] [Abstract][Full Text] [Related]
4. Improved lung nodule diagnosis accuracy using lung CT images with uncertain class. Wang Z; Xin J; Sun P; Lin Z; Yao Y; Gao X Comput Methods Programs Biomed; 2018 Aug; 162():197-209. PubMed ID: 29903487 [TBL] [Abstract][Full Text] [Related]
5. Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features. Way TW; Sahiner B; Chan HP; Hadjiiski L; Cascade PN; Chughtai A; Bogot N; Kazerooni E Med Phys; 2009 Jul; 36(7):3086-98. PubMed ID: 19673208 [TBL] [Abstract][Full Text] [Related]
6. Comparative evaluation of support vector machine classification for computer aided detection of breast masses in mammography. Lesniak JM; Hupse R; Blanc R; Karssemeijer N; Székely G Phys Med Biol; 2012 Aug; 57(16):5295-307. PubMed ID: 22853938 [TBL] [Abstract][Full Text] [Related]
7. Feature fusion for lung nodule classification. Farag AA; Ali A; Elshazly S; Farag AA Int J Comput Assist Radiol Surg; 2017 Oct; 12(10):1809-1818. PubMed ID: 28623478 [TBL] [Abstract][Full Text] [Related]
8. Computer-aided diagnosis of lung cancer: the effect of training data sets on classification accuracy of lung nodules. Gong J; Liu JY; Sun XW; Zheng B; Nie SD Phys Med Biol; 2018 Feb; 63(3):035036. PubMed ID: 29311420 [TBL] [Abstract][Full Text] [Related]
9. Computer-aided lung nodule recognition by SVM classifier based on combination of random undersampling and SMOTE. Sui Y; Wei Y; Zhao D Comput Math Methods Med; 2015; 2015():368674. PubMed ID: 25977704 [TBL] [Abstract][Full Text] [Related]
10. Computer-aided diagnosis for early-stage lung cancer based on longitudinal and balanced data. Sun T; Zhang R; Wang J; Li X; Guo X PLoS One; 2013; 8(5):e63559. PubMed ID: 23691066 [TBL] [Abstract][Full Text] [Related]
11. Fully automatic detection of lung nodules in CT images using a hybrid feature set. Shaukat F; Raja G; Gooya A; Frangi AF Med Phys; 2017 Jul; 44(7):3615-3629. PubMed ID: 28409834 [TBL] [Abstract][Full Text] [Related]
12. Feature selection and performance evaluation of support vector machine (SVM)-based classifier for differentiating benign and malignant pulmonary nodules by computed tomography. Zhu Y; Tan Y; Hua Y; Wang M; Zhang G; Zhang J J Digit Imaging; 2010 Feb; 23(1):51-65. PubMed ID: 19242759 [TBL] [Abstract][Full Text] [Related]
13. A novel computer-aided lung nodule detection system for CT images. Tan M; Deklerck R; Jansen B; Bister M; Cornelis J Med Phys; 2011 Oct; 38(10):5630-45. PubMed ID: 21992380 [TBL] [Abstract][Full Text] [Related]
14. A weighted rule based method for predicting malignancy of pulmonary nodules by nodule characteristics. Kaya A; Can AB J Biomed Inform; 2015 Aug; 56():69-79. PubMed ID: 26008877 [TBL] [Abstract][Full Text] [Related]
15. Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine. Madero Orozco H; Vergara Villegas OO; Cruz Sánchez VG; Ochoa Domínguez Hde J; Nandayapa Alfaro Mde J Biomed Eng Online; 2015 Feb; 14():9. PubMed ID: 25888834 [TBL] [Abstract][Full Text] [Related]
16. Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model. Tan M; Pu J; Zheng B Int J Comput Assist Radiol Surg; 2014 Nov; 9(6):1005-20. PubMed ID: 24664267 [TBL] [Abstract][Full Text] [Related]
17. Predicting adenocarcinoma recurrence using computational texture models of nodule components in lung CT. Depeursinge A; Yanagawa M; Leung AN; Rubin DL Med Phys; 2015 Apr; 42(4):2054-63. PubMed ID: 25832095 [TBL] [Abstract][Full Text] [Related]
18. Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification. Lee HS; Hong H; Jung DC; Park S; Kim J Med Phys; 2017 Jul; 44(7):3604-3614. PubMed ID: 28376281 [TBL] [Abstract][Full Text] [Related]
19. Multistage segmentation model and SVM-ensemble for precise lung nodule detection. Naqi SM; Sharif M; Yasmin M Int J Comput Assist Radiol Surg; 2018 Jul; 13(7):1083-1095. PubMed ID: 29492880 [TBL] [Abstract][Full Text] [Related]
20. A study of computer-aided diagnosis for pulmonary nodule: comparison between classification accuracies using calculated image features and imaging findings annotated by radiologists. Kawagishi M; Chen B; Furukawa D; Sekiguchi H; Sakai K; Kubo T; Yakami M; Fujimoto K; Sakamoto R; Emoto Y; Aoyama G; Iizuka Y; Nakagomi K; Yamamoto H; Togashi K Int J Comput Assist Radiol Surg; 2017 May; 12(5):767-776. PubMed ID: 28285338 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]