Automatic pulmonary nodule detection on computed tomography images using novel deep learning
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17502-3.pdf
Reference42 articles.
1. Naqi SM, Sharif M, Jaffar A (2020) Lung nodule detection and classification based on geometric fit in parametric form and deep learning. Neural Comput Appl 32(9):4629–4647. https://doi.org/10.1007/s00521-018-3773-x
2. Zhou Z, et al (2018) Unet++: A nested u-net architecture for medical image segmentation, in Deep learning in medical image analysis and multimodal learning for clinical decision support, Springer. p. 3-11. https://doi.org/10.1007/978-3-030-00889-5_1
3. Shi J (2018) Lung nodule detection using convolutional neural networks. Electrical Engineering and Computer Sciences. University of California at Berkeley, Berkeley, California
4. Jung H et al (2018) Classification of lung nodules in CT scans using three-dimensional deep convolutional neural networks with a checkpoint ensemble method. BMC Med Imaging 18(1):1–10. https://doi.org/10.1186/s12880-018-0286-0
5. Liu X et al (2018) Multi-view multi-scale CNNs for lung nodule type classification from CT images. Pattern Recog 77:262–275. https://doi.org/10.1016/j.patcog.2017.12.022
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