186 related articles for article (PubMed ID: 32400982)
1. [Application of Radiomics in Classification and Prediction of Benign and Malignant Lung Tumors].
Zhou T; Zhu C; Shi F
Zhongguo Yi Liao Qi Xie Za Zhi; 2020 Feb; 44(2):113-117. PubMed ID: 32400982
[TBL] [Abstract][Full Text] [Related]
2. Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer.
Choi W; Oh JH; Riyahi S; Liu CJ; Jiang F; Chen W; White C; Rimner A; Mechalakos JG; Deasy JO; Lu W
Med Phys; 2018 Apr; 45(4):1537-1549. PubMed ID: 29457229
[TBL] [Abstract][Full Text] [Related]
3. Computer-aided diagnosis of ground glass pulmonary nodule by fusing deep learning and radiomics features.
Hu X; Gong J; Zhou W; Li H; Wang S; Wei M; Peng W; Gu Y
Phys Med Biol; 2021 Mar; 66(6):065015. PubMed ID: 33596552
[TBL] [Abstract][Full Text] [Related]
4. Localized thin-section CT with radiomics feature extraction and machine learning to classify early-detected pulmonary nodules from lung cancer screening.
Tu SJ; Wang CW; Pan KT; Wu YC; Wu CT
Phys Med Biol; 2018 Mar; 63(6):065005. PubMed ID: 29446758
[TBL] [Abstract][Full Text] [Related]
5. External validation of radiomics-based predictive models in low-dose CT screening for early lung cancer diagnosis.
Garau N; Paganelli C; Summers P; Choi W; Alam S; Lu W; Fanciullo C; Bellomi M; Baroni G; Rampinelli C
Med Phys; 2020 Sep; 47(9):4125-4136. PubMed ID: 32488865
[TBL] [Abstract][Full Text] [Related]
6. A multi-instance tumor subtype classification method for small PET datasets using RA-DL attention module guided deep feature extraction with radiomics features.
Diao Z; Jiang H
Comput Biol Med; 2024 May; 174():108461. PubMed ID: 38626509
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Parameter tuning in machine learning based on radiomics biomarkers of lung cancer.
Luo Y; Li Y; Zhang Y; Zhang J; Liang M; Jiang L; Guo L
J Xray Sci Technol; 2022; 30(3):477-490. PubMed ID: 35342074
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Radiomics for Classification of Lung Cancer Histological Subtypes Based on Nonenhanced Computed Tomography.
E L; Lu L; Li L; Yang H; Schwartz LH; Zhao B
Acad Radiol; 2019 Sep; 26(9):1245-1252. PubMed ID: 30502076
[TBL] [Abstract][Full Text] [Related]
11. Classification of benign and malignant lung nodules from CT images based on hybrid features.
Zhang G; Yang Z; Gong L; Jiang S; Wang L
Phys Med Biol; 2019 Jun; 64(12):125011. PubMed ID: 31141794
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. 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]
14. Multiclassifier fusion based on radiomics features for the prediction of benign and malignant primary pulmonary solid nodules.
Shen Y; Xu F; Zhu W; Hu H; Chen T; Li Q
Ann Transl Med; 2020 Mar; 8(5):171. PubMed ID: 32309318
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images.
Dhara AK; Mukhopadhyay S; Dutta A; Garg M; Khandelwal N
J Digit Imaging; 2016 Aug; 29(4):466-75. PubMed ID: 26738871
[TBL] [Abstract][Full Text] [Related]
17. Potential feature exploration and model development based on 18F-FDG PET/CT images for differentiating benign and malignant lung lesions.
Zhang R; Zhu L; Cai Z; Jiang W; Li J; Yang C; Yu C; Jiang B; Wang W; Xu W; Chai X; Zhang X; Tang Y
Eur J Radiol; 2019 Dec; 121():108735. PubMed ID: 31733432
[TBL] [Abstract][Full Text] [Related]
18. Effect of CT Reconstruction Algorithm on the Diagnostic Performance of Radiomics Models: A Task-Based Approach for Pulmonary Subsolid Nodules.
Kim H; Park CM; Gwak J; Hwang EJ; Lee SY; Jung J; Hong H; Goo JM
AJR Am J Roentgenol; 2019 Mar; 212(3):505-512. PubMed ID: 30476456
[TBL] [Abstract][Full Text] [Related]
19. Radiomics analysis for distinctive identification of COVID-19 pulmonary nodules from other benign and malignant counterparts.
Selvam M; Chandrasekharan A; Sadanandan A; Anand VK; Ramesh S; Murali A; Krishnamurthi G
Sci Rep; 2024 Mar; 14(1):7079. PubMed ID: 38528100
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
