217 related articles for article (PubMed ID: 37971691)
21. Perinodular Parenchymal Features Improve Indeterminate Lung Nodule Classification.
Masquelin AH; Alshaabi T; Cheney N; Estépar RSJ; Bates JHT; Kinsey CM
Acad Radiol; 2023 Jun; 30(6):1073-1080. PubMed ID: 35933282
[TBL] [Abstract][Full Text] [Related]
22. 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]
23. Multitask network for thyroid nodule diagnosis based on TI-RADS.
Han X; Chang L; Song K; Cheng L; Li M; Wei X
Med Phys; 2022 Aug; 49(8):5064-5080. PubMed ID: 35608232
[TBL] [Abstract][Full Text] [Related]
24. Multi-Layer Perceptron Classifier with the Proposed Combined Feature Vector of 3D CNN Features and Lung Radiomics Features for COPD Stage Classification.
Yang Y; Zeng N; Chen Z; Li W; Guo Y; Wang S; Duan W; Liu Y; Chen R; Kang Y
J Healthc Eng; 2023; 2023():3715603. PubMed ID: 37953910
[TBL] [Abstract][Full Text] [Related]
25. Deep-Learning Model of ResNet Combined with CBAM for Malignant-Benign Pulmonary Nodules Classification on Computed Tomography Images.
Zhang Y; Feng W; Wu Z; Li W; Tao L; Liu X; Zhang F; Gao Y; Huang J; Guo X
Medicina (Kaunas); 2023 Jun; 59(6):. PubMed ID: 37374292
[No Abstract] [Full Text] [Related]
26. 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]
27. A comparison of the fusion model of deep learning neural networks with human observation for lung nodule detection and classification.
Gürsoy Çoruh A; Yenigün B; Uzun Ç; Kahya Y; Büyükceran EU; Elhan A; Orhan K; Kayı Cangır A
Br J Radiol; 2021 Jul; 94(1123):20210222. PubMed ID: 34111976
[TBL] [Abstract][Full Text] [Related]
28. One-step algorithm for fast-track localization and multi-category classification of histological subtypes in lung cancer.
Qi J; Deng Z; Sun G; Qian S; Liu L; Xu B
Eur J Radiol; 2022 Sep; 154():110443. PubMed ID: 35901600
[TBL] [Abstract][Full Text] [Related]
29. 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]
30. Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT.
Uthoff J; Stephens MJ; Newell JD; Hoffman EA; Larson J; Koehn N; De Stefano FA; Lusk CM; Wenzlaff AS; Watza D; Neslund-Dudas C; Carr LL; Lynch DA; Schwartz AG; Sieren JC
Med Phys; 2019 Jul; 46(7):3207-3216. PubMed ID: 31087332
[TBL] [Abstract][Full Text] [Related]
31. Multi-view secondary input collaborative deep learning for lung nodule 3D segmentation.
Dong X; Xu S; Liu Y; Wang A; Saripan MI; Li L; Zhang X; Lu L
Cancer Imaging; 2020 Aug; 20(1):53. PubMed ID: 32738913
[TBL] [Abstract][Full Text] [Related]
32. Discrimination between transient and persistent subsolid pulmonary nodules on baseline CT using deep transfer learning.
Huang C; Lv W; Zhou C; Mao L; Xu Q; Li X; Qi L; Xia F; Li X; Zhang Q; Zhang L; Lu G
Eur Radiol; 2020 Dec; 30(12):6913-6923. PubMed ID: 32696253
[TBL] [Abstract][Full Text] [Related]
33. A Novel Hybridized Feature Extraction Approach for Lung Nodule Classification Based on Transfer Learning Technique.
Bruntha PM; Pandian SIA; Anitha J; Abraham SS; Kumar SN
J Med Phys; 2022; 47(1):1-9. PubMed ID: 35548037
[TBL] [Abstract][Full Text] [Related]
34. Identification of Benign and Malignant Lung Nodules in CT Images Based on Ensemble Learning Method.
Xu Y; Wang S; Sun X; Yang Y; Fan J; Jin W; Li Y; Su F; Zhang W; Cui Q; Hu Y; Wang S; Zhang J; Chen C
Interdiscip Sci; 2022 Mar; 14(1):130-140. PubMed ID: 34727340
[TBL] [Abstract][Full Text] [Related]
35. Res-trans networks for lung nodule classification.
Liu D; Liu F; Tie Y; Qi L; Wang F
Int J Comput Assist Radiol Surg; 2022 Jun; 17(6):1059-1068. PubMed ID: 35290646
[TBL] [Abstract][Full Text] [Related]
36. Semi-Supervised Deep Transfer Learning for Benign-Malignant Diagnosis of Pulmonary Nodules in Chest CT Images.
Shi F; Chen B; Cao Q; Wei Y; Zhou Q; Zhang R; Zhou Y; Yang W; Wang X; Fan R; Yang F; Chen Y; Li W; Gao Y; Shen D
IEEE Trans Med Imaging; 2022 Apr; 41(4):771-781. PubMed ID: 34705640
[TBL] [Abstract][Full Text] [Related]
37. Lung nodule malignancy classification with associated pulmonary fibrosis using 3D attention-gated convolutional network with CT scans.
Liu Y; Hsu HY; Lin T; Peng B; Saqi A; Salvatore MM; Jambawalikar S
J Transl Med; 2024 Jan; 22(1):51. PubMed ID: 38216992
[TBL] [Abstract][Full Text] [Related]
38. Classification of Benign and Malignant Lung Nodules Based on Deep Convolutional Network Feature Extraction.
Lv E; Liu W; Wen P; Kang X
J Healthc Eng; 2021; 2021():8769652. PubMed ID: 34745513
[TBL] [Abstract][Full Text] [Related]
39. Deep learning combined with radiomics may optimize the prediction in differentiating high-grade lung adenocarcinomas in ground glass opacity lesions on CT scans.
Wang X; Zhang L; Yang X; Tang L; Zhao J; Chen G; Li X; Yan S; Li S; Yang Y; Kang Y; Li Q; Wu N
Eur J Radiol; 2020 Aug; 129():109150. PubMed ID: 32604042
[TBL] [Abstract][Full Text] [Related]
40. The classification of benign and malignant lung nodules based on CT radiomics: a systematic review, quality score assessment, and meta-analysis.
Zhu F; Yang C; Zou J; Ma W; Wei Y; Zhao Z
Acta Radiol; 2023 Dec; 64(12):3074-3084. PubMed ID: 37817511
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
