508 related articles for article (PubMed ID: 31141794)
1. 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]
2. Classification of lung nodules based on CT images using squeeze-and-excitation network and aggregated residual transformations.
Zhang G; Yang Z; Gong L; Jiang S; Wang L; Zhang H
Radiol Med; 2020 Apr; 125(4):374-383. PubMed ID: 31916105
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. 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]
6. An improved 3-D attention CNN with hybrid loss and feature fusion for pulmonary nodule classification.
Huang YS; Wang TC; Huang SZ; Zhang J; Chen HM; Chang YC; Chang RF
Comput Methods Programs Biomed; 2023 Feb; 229():107278. PubMed ID: 36463674
[TBL] [Abstract][Full Text] [Related]
7. Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours.
Way TW; Hadjiiski LM; Sahiner B; Chan HP; Cascade PN; Kazerooni EA; Bogot N; Zhou C
Med Phys; 2006 Jul; 33(7):2323-37. PubMed ID: 16898434
[TBL] [Abstract][Full Text] [Related]
8. Lung nodule classification using deep Local-Global networks.
Al-Shabi M; Lan BL; Chan WY; Ng KH; Tan M
Int J Comput Assist Radiol Surg; 2019 Oct; 14(10):1815-1819. PubMed ID: 31020576
[TBL] [Abstract][Full Text] [Related]
9. Incorporating automatically learned pulmonary nodule attributes into a convolutional neural network to improve accuracy of benign-malignant nodule classification.
Dai Y; Yan S; Zheng B; Song C
Phys Med Biol; 2018 Dec; 63(24):245004. PubMed ID: 30524071
[TBL] [Abstract][Full Text] [Related]
10. Automated Pulmonary Nodule Classification in Computed Tomography Images Using a Deep Convolutional Neural Network Trained by Generative Adversarial Networks.
Onishi Y; Teramoto A; Tsujimoto M; Tsukamoto T; Saito K; Toyama H; Imaizumi K; Fujita H
Biomed Res Int; 2019; 2019():6051939. PubMed ID: 30719445
[TBL] [Abstract][Full Text] [Related]
11. Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss.
Tran GS; Nghiem TP; Nguyen VT; Luong CM; Burie JC
J Healthc Eng; 2019; 2019():5156416. PubMed ID: 30863524
[TBL] [Abstract][Full Text] [Related]
12. Neural network-based computer-aided diagnosis in distinguishing malignant from benign solitary pulmonary nodules by computed tomography.
Chen H; Wang XH; Ma DQ; Ma BR
Chin Med J (Engl); 2007 Jul; 120(14):1211-5. PubMed ID: 17697569
[TBL] [Abstract][Full Text] [Related]
13. Automated lung nodule classification following automated nodule detection on CT: a serial approach.
Armato SG; Altman MB; Wilkie J; Sone S; Li F; Doi K; Roy AS
Med Phys; 2003 Jun; 30(6):1188-97. PubMed ID: 12852543
[TBL] [Abstract][Full Text] [Related]
14. Dual-branch feature fusion S3D V-Net network for lung nodules segmentation.
Xu X; Du L; Yin D
J Appl Clin Med Phys; 2024 Jun; 25(6):e14331. PubMed ID: 38478388
[TBL] [Abstract][Full Text] [Related]
15. Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies.
Nasrullah N; Sang J; Alam MS; Mateen M; Cai B; Hu H
Sensors (Basel); 2019 Aug; 19(17):. PubMed ID: 31466261
[TBL] [Abstract][Full Text] [Related]
16. An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT.
Alilou M; Beig N; Orooji M; Rajiah P; Velcheti V; Rakshit S; Reddy N; Yang M; Jacono F; Gilkeson RC; Linden P; Madabhushi A
Med Phys; 2017 Jul; 44(7):3556-3569. PubMed ID: 28295386
[TBL] [Abstract][Full Text] [Related]
17. A novel higher order appearance texture analysis to diagnose lung cancer based on a modified local ternary pattern.
Alksas A; Shaffie A; Ghazal M; Taher F; Khelifi A; Yaghi M; Soliman A; Bogaert EV; El-Baz A
Comput Methods Programs Biomed; 2023 Oct; 240():107692. PubMed ID: 37459773
[TBL] [Abstract][Full Text] [Related]
18. Malignant-benign classification of pulmonary nodules based on random forest aided by clustering analysis.
Wu W; Hu H; Gong J; Li X; Huang G; Nie S
Phys Med Biol; 2019 Jan; 64(3):035017. PubMed ID: 30702087
[TBL] [Abstract][Full Text] [Related]
19. 3D SAACNet with GBM for the classification of benign and malignant lung nodules.
Guo Z; Yang J; Zhao L; Yuan J; Yu H
Comput Biol Med; 2023 Feb; 153():106532. PubMed ID: 36623436
[TBL] [Abstract][Full Text] [Related]
20. Semi-supervised adversarial model for benign-malignant lung nodule classification on chest CT.
Xie Y; Zhang J; Xia Y
Med Image Anal; 2019 Oct; 57():237-248. PubMed ID: 31352126
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]