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2000
Volume 21, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Objective

To address the low efficiency of diagnosing pulmonary nodules using computed tomography (CT) images and the difficulty in obtaining the key signs of malignant pulmonary nodules, a ghost convolution residual network incorporating hybrid normalization (GCHN-net) is proposed.

Methods

Firstly, a three-dimensional ghost convolution with a small kernel is embedded in the GCHN-net. Secondly, we designed a hybrid normalized-activation module (TMNAM) that can handle the rich and complex features of lung nodules in both the deep and shallow layers of the network, and incorporating two different normalization methods. This allows the network to comprehensively learn the intricate relationships underlying the intrinsic features of lung nodules and enhances its capacity to classify the properties of unknown nodules. Additionally, to enhance the accuracy and detail of the category activation map, GradCAM++ is integrated into the third layer of the GCHN-net. This integration enables the visualization of specific regions within three-dimensional lung nodules that the model focuses on during its predictions.

Results

The accuracy of the GCHN-net on the Lung Nodule Analysis 16 (LUNA16) dataset was 90.22%, with an F1-score of 88.31% and a G-mean of 90.48%.

Conclusion

Compared with existing methods, the proposed method can greatly improve the classification of pulmonary nodules and can effectively assist doctors in diagnosing patients with pulmonary nodules.

2025 © 2025 The Author(s). Published by Bentham Science Publisher. The Author(s)
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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References

  1. HendersonL.M. RiveraM.P. BaschE. Broadened eligibility for lung cancer screening: challenges and uncertainty for implementation and equity.JAMA20213251093994110.1001/jama.2020.2642233687453
     [Google Scholar]
  2. SungH. FerlayJ. SiegelR.L. LaversanneM. SoerjomataramI. JemalA. BrayF. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J. Clin.202171320924910.3322/caac.2166033538338
     [Google Scholar]
  3. RanaR.H. AlamF. AlamK. GowJ. Gender-specific differences in care-seeking behaviour among lung cancer patients: A systematic review.J. Cancer Res. Clin. Oncol.202014651169119610.1007/s00432‑020‑03197‑832246217
     [Google Scholar]
  4. SilveyraP. FuentesN. Rodriguez BauzaD.E. Sex and Gender Differences in Lung Disease.ChamSpringer International Publishing202122725810.1007/978‑3‑030‑68748‑9_14
     [Google Scholar]
  5. GuY. LuX. YangL. ZhangB. YuD. ZhaoY. GaoL. WuL. ZhouT. Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs.Comput. Biol. Med.201810322023110.1016/j.compbiomed.2018.10.01130390571
     [Google Scholar]
  6. Casal-MouriñoA. Ruano-RavinaA. Lorenzo-GonzálezM. Rodríguez-MartínezÁ. Giraldo-OsorioA. Varela-LemaL. Pereiro-BreaT. Barros-DiosJ.M. Valdés-CuadradoL. Pérez-RíosM. Epidemiology of stage III lung cancer: Frequency, diagnostic characteristics, and survival.Transl. Lung Cancer Res.202110150651810.21037/tlcr.2020.03.4033569332
     [Google Scholar]
  7. LiC. WangH. JiangY. FuW. LiuX. ZhongR. ChengB. ZhuF. XiangY. HeJ. LiangW. Advances in lung cancer screening and early detection.Cancer Biol. Med.202219559160810.20892/j.issn.2095‑3941.2021.069035535966
     [Google Scholar]
  8. GuY. ChiJ. LiuJ. YangL. ZhangB. YuD. ZhaoY. LuX. A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning.Comput. Biol. Med.202113710480610.1016/j.compbiomed.2021.10480634461501
     [Google Scholar]
  9. FitzgeraldR.C. AntoniouA.C. FrukL. RosenfeldN. The future of early cancer detection.Nat. Med.202228466667710.1038/s41591‑022‑01746‑x35440720
     [Google Scholar]
  10. CrosbyD. BhatiaS. BrindleK.M. CoussensL.M. DiveC. EmbertonM. EsenerS. FitzgeraldR.C. GambhirS.S. KuhnP. RebbeckT.R. BalasubramanianS. Early detection of cancer.Science20223756586eaay904010.1126/science.aay904035298272
     [Google Scholar]
  11. GuY. LuX. ZhangB. ZhaoY. YuD. GaoL. CuiG. WuL. ZhouT. Automatic lung nodule detection using multi-scale dot nodule-enhancement filter and weighted support vector machines in chest computed tomography.PLoS One2019141e021055110.1371/journal.pone.021055130629724
     [Google Scholar]
  12. WangY. WuB. ZhangN. LiuJ. RenF. ZhaoL. Research progress of computer aided diagnosis system for pulmonary nodules in CT images.J. XRay Sci. Technol.202028111610.3233/XST‑19058131815727
     [Google Scholar]
  13. JinH. YuC. GongZ. ZhengR. ZhaoY. FuQ. Machine learning techniques for pulmonary nodule computer-aided diagnosis using CT images: A systematic review.Biomed. Signal Process. Control20237910410410.1016/j.bspc.2022.104104
     [Google Scholar]
  14. ZuoW. ZhouF. LiZ. WangL. Multi-resolution CNN and knowledge transfer for candidate classification in lung nodule detection.IEEE Access20197325103252110.1109/ACCESS.2019.2903587
     [Google Scholar]
  15. WatayaT. YanagawaM. TsubamotoM. SatoT. NishigakiD. KitaK. YamagataK. SuzukiY. HataA. KidoS. TomiyamaN. Radiologists with and without deep learning–based computer-aided diagnosis: Comparison of performance and interobserver agreement for characterizing and diagnosing pulmonary nodules/masses.Eur. Radiol.202233134835910.1007/s00330‑022‑08948‑435751697
     [Google Scholar]
  16. ParkS. ParkH. LeeS.M. AhnY. KimW. JungK. SeoJ.B. Application of computer-aided diagnosis for Lung-RADS categorization in CT screening for lung cancer: effect on inter-reader agreement.Eur. Radiol.20223221054106410.1007/s00330‑021‑08202‑334331112
     [Google Scholar]
  17. ChanH.P. HadjiiskiL.M. SamalaR.K. Computer‐aided diagnosis in the era of deep learning.Med. Phys.2020475e218e22710.1002/mp.1376432418340
     [Google Scholar]
  18. PainuliD. BhardwajS. köseU. Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review.Comput. Biol. Med.202214610558010.1016/j.compbiomed.2022.10558035551012
     [Google Scholar]
  19. UsmanM. RehmanA. ShahidA. LatifS. ShinY. MEDS-Net: Multi-encoder based self-distilled network with bidirectional maximum intensity projections fusion for lung nodule detection.Eng. Appl. Artif. Intell.202412910759710.1016/j.engappai.2023.107597
     [Google Scholar]
  20. MkinduH. WuL. ZhaoY. Lung nodule detection in chest CT images based on vision transformer network with Bayesian optimization.Biomed. Signal Process. Control20238510486610.1016/j.bspc.2023.104866
     [Google Scholar]
  21. BaoQ.Q. TangS.Y. LiJ.Q. Improve the YOLOv8 model for multi-type lung nodule detection.J. Front. Comput. Sci. Technol.2024http://kns.cnki.net/kcms/detail/11.5602.tp.20240904.1819.002.html
     [Google Scholar]
  22. GuY. ChiJ.Q. ZhangB.H. Lung cancer CT-assisted diagnosis based on improved residual and attention.Transducer and Microsystem Technology20244393034 https://d.wanfangdata.com.cn/periodical/cgqjs202409008
    [Google Scholar]
  23. UsmanM. RehmanA. ShahidA. LatifS. ByonS.S. KimS.H. KhanT.M. ShinY.G.J.a. MESAHA-Net: Multi-encoders based self-adaptive hard attention network with maximum intensity projections for lung nodule segmentation in CT scan.arXiv20232023
     [Google Scholar]
  24. MurugappanM. BourislyA.K. PrakashN. SumithraM. AcharyaU. Automated semantic lung segmentation in chest CT images using deep neural network.Neural Comput Appl.20233521153431536410.1007/s00521‑023‑08407‑1
     [Google Scholar]
  25. CaoH. LiuH. SongE. MaG. JinR. XuX. LiuT. HungC.C. A two-stage convolutional neural networks for lung nodule detection.IEEE J. Biomed. Health Inform.2020247110.1109/JBHI.2019.296372031905154
     [Google Scholar]
  26. XieY. XiaY. ZhangJ. SongY. FengD. FulhamM. CaiW. Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT.IEEE Trans. Med. Imaging2019384991100410.1109/TMI.2018.287651030334786
     [Google Scholar]
  27. XieY. ZhangJ. XiaY. FulhamM. ZhangY. Fusing texture, shape and deep model-learned information at decision level for automated classification of lung nodules on chest CT.Inf. Fusion20184210211010.1016/j.inffus.2017.10.005
     [Google Scholar]
  28. WangH. ZhuH. DingL. Accurate classification of lung nodules on CT images using the TransUnet.Front Public Health.202210106079810.3389/fpubh.2022.1060798
     [Google Scholar]
  29. ZhaoX. LiuL. QiS. TengY. LiJ. QianW. Agile convolutional neural network for pulmonary nodule classification using CT images.Int. J. CARS201813458559510.1007/s11548‑017‑1696‑029473129
     [Google Scholar]
  30. LimaL.L. FerreiraJ.R.Junior OliveiraM. Toward classifying small lung nodules with hyperparameter optimization of convolutional neural networks.Comput. Intell.20213741599161810.1111/coin.12350
     [Google Scholar]
  31. AliI. MuzammilM. HaqI.U. AmirM. AbdullahS. Efficient lung nodule classification using transferable texture convolutional neural network.IEEE Access2020817585917587010.1109/ACCESS.2020.3026080
     [Google Scholar]
  32. JiangH. GaoF. XuX. HuangF. ZhuS. Attentive and ensemble 3D dual path networks for pulmonary nodules classification.Neurocomputing202039842243010.1016/j.neucom.2019.03.103
     [Google Scholar]
  33. Al-ShabiM. LanB.L. ChanW.Y. NgK.H. TanM. Lung nodule classification using deep Local–Global networks.Int. J. CARS201914101815181910.1007/s11548‑019‑01981‑731020576
     [Google Scholar]
  34. LiY.F LuoY GuoL LiangM. Application of radiomics analysis and machine learning in the classification of benign and malignant pulmonary nodules.Radiology Practice20212021446446910.13609/j.cnki.1000‑0313.2021.04.009
     [Google Scholar]
  35. WangY. ZhangH. ChaeK.J. ChoiY. JinG.Y. KoS.B. Novel convolutional neural network architecture for improved pulmonary nodule classification on computed tomography.Multidimens. Syst. Signal Process.20203131163118310.1007/s11045‑020‑00703‑6
     [Google Scholar]
  36. WangH. ZhaoT. LiL.C. PanH. LiuW. GaoH. HanF. WangY. QiY. LiangZ. A hybrid CNN feature model for pulmonary nodule malignancy risk differentiation.J. XRay Sci. Technol.201826217118710.3233/XST‑1730229036877
     [Google Scholar]
  37. ChenS. HanY. LinJ. ZhaoX. KongP. Pulmonary nodule detection on chest radiographs using balanced convolutional neural network and classic candidate detection.Artif. Intell. Med.202010710188110.1016/j.artmed.2020.10188132828440
     [Google Scholar]
  38. PetriniL. CagnettaF. Vanden-EijndenE. WyartM. Learning sparse features can lead to overfitting in neural networks.J. Stat. Mech.2023114003
     [Google Scholar]
  39. MoradiR. BerangiR. MinaeiB. A survey of regularization strategies for deep models.Artif. Intell. Rev.20205363947398610.1007/s10462‑019‑09784‑7
     [Google Scholar]
  40. AsghariV. LeungY.F. HsuS.C. Deep neural network based framework for complex correlations in engineering metrics.Adv. Eng. Inform.20204410105810.1016/j.aei.2020.101058
     [Google Scholar]
  41. ZhaiP. TaoY. ChenH. CaiT. LiJ. Multi-Task Learning for Lung Nodule Classification on Chest CT.IEEE Access2020818031718032710.1109/ACCESS.2020.3027812
     [Google Scholar]
  42. DongX. XuS. LiuY. WangA. SaripanM.I. LiL. ZhangX. LuL. Multi-view secondary input collaborative deep learning for lung nodule 3D segmentation.Cancer Imaging20202015310.1186/s40644‑020‑00331‑032738913
     [Google Scholar]
  43. ZhiL. JiangW. ZhangS. ZhouT. Deep neural network pulmonary nodule segmentation methods for CT images: Literature review and experimental comparisons.Comput. Biol. Med.202316410732110.1016/j.compbiomed.2023.10732137595518
     [Google Scholar]
  44. WuL. BakS. ShinY. ChuY.S. YooS. RobinsonI.K. HuangX. Resolution-enhanced X-ray fluorescence microscopy via deep residual networks.NPJ Comput. Mater.20239310.1038/s41524‑023‑00995‑9
     [Google Scholar]
  45. XueZ. YuX. LiuB. TanX. WeiX. HResNetAM: Hierarchical residual network with attention mechanism for hyperspectral image classification.IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.2021143566358010.1109/JSTARS.2021.3065987
     [Google Scholar]
  46. ZhangX. LiW. GaoC. YangY. ChangK. Hyperspectral pathology image classification using dimension-driven multi-path attention residual network.Expert Syst. Appl.202323012061510.1016/j.eswa.2023.120615
     [Google Scholar]
  47. GalvánE. MooneyP. Neuroevolution in deep neural networks: Current trends and future challenges.IEEE Trans. Artif. Intell.20212647649310.1109/TAI.2021.3067574
     [Google Scholar]
  48. ZhanZ.H. LiJ.Y. ZhangJ. Evolutionary deep learning: A survey.Neurocomputing2022483425810.1016/j.neucom.2022.01.099
     [Google Scholar]
  49. DarwishA. HassanienA.E. DasS. A survey of swarm and evolutionary computing approaches for deep learning.Artif. Intell. Rev.20205331767181210.1007/s10462‑019‑09719‑2
     [Google Scholar]
  50. HanK. WangY. TianQ. GuoJ. XuC. GhostNet: More features from cheap operations.2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA20201577158610.1109/CVPR42600.2020.00165
     [Google Scholar]
  51. ZhouL. LiangL. ShengX. GA-Net: Ghost convolution adaptive fusion skin lesion segmentation network.Comput. Biol. Med.202316410727310.1016/j.compbiomed.2023.10727337562327
     [Google Scholar]
  52. ShariatyF. OroojiM. VelichkoE.N. ZavjalovS.V. Texture appearance model, a new model-based segmentation paradigm, application on the segmentation of lung nodule in the CT scan of the chest.Comput. Biol. Med.202214010508610.1016/j.compbiomed.2021.10508634861641
     [Google Scholar]
  53. MonkamP. QiS. XuM. HanF. ZhaoX. QianW. CNN models discriminating between pulmonary micro-nodules and non-nodules from CT images.Biomed. Eng. Online20181719610.1186/s12938‑018‑0529‑x30012167
     [Google Scholar]
  54. LiangT. GlossnerJ. WangL. ShiS. ZhangX. Pruning and quantization for deep neural network acceleration: A survey.Neurocomputing202146137040310.1016/j.neucom.2021.07.045
     [Google Scholar]
  55. ZhaoB. ZhangX. LiH. YangZ. Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions.Knowl. Base. Syst.202019910597110.1016/j.knosys.2020.105971
     [Google Scholar]
  56. BittnerK. AdamF. CuiS. KörnerM. ReinartzP. Building footprint extraction from VHR remote sensing images combined with normalized DSMs using fused fully convolutional networks.IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.20181182615262910.1109/JSTARS.2018.2849363
     [Google Scholar]
  57. IoffeS. SzegedyC. Batch normalization: Accelerating deep network training by reducing internal covariate shift.Proceedings of the 32nd International Conference on Machine Learning201537448456
     [Google Scholar]
  58. BronskillJ. GordonJ. RequeimaJ. NowozinS. TurnerR. TaskNorm: Rethinking batch normalization for meta-learning.Proceedings of the 37th International Conference on Machine Learning.Vienna, Austria202011911531164
     [Google Scholar]
  59. SeguM. TonioniA. TombariF. Batch normalization embeddings for deep domain generalization.Pattern Recognit.202313510911510.1016/j.patcog.2022.109115
     [Google Scholar]
  60. LakshmiT.R.V. Krishna ReddyC.V. Classification of skin lesions by incorporating drop-block and batch normalization layers in representative CNN models.Arab. J. Sci. Eng.20244933671368410.1007/s13369‑023‑08131‑x
     [Google Scholar]
  61. HuangL. QinJ. ZhouY. ZhuF. LiuL. ShaoL. Normalization techniques in training DNNs: Methodology, analysis and application.IEEE Trans. Pattern Anal. Mach. Intell.2023458101731019610.1109/TPAMI.2023.325024137027763
     [Google Scholar]
  62. SetioA.A.A. TraversoA. de BelT. BerensM.S.N. BogaardC. CerelloP. ChenH. DouQ. FantacciM.E. GeurtsB. GugtenR. HengP.A. JansenB. de KasteM.M.J. KotovV. LinJ.Y.H. MandersJ.T.M.C. Sóñora-MenganaA. García-NaranjoJ.C. PapavasileiouE. ProkopM. SalettaM. Schaefer-ProkopC.M. ScholtenE.T. ScholtenL. SnoerenM.M. TorresE.L. VandemeulebrouckeJ. WalasekN. ZuidhofG.C.A. GinnekenB. JacobsC. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge.Med. Image Anal.20174211310.1016/j.media.2017.06.01528732268
     [Google Scholar]
  63. LinC.Y. GuoS.M. LienJ.J.J. TsaiT.Y. LiuY.S. LaiC.H. HsuI.L. ChangC.C. TsengY.L. Development of a modified 3D region proposal network for lung nodule detection in computed tomography scans: A secondary analysis of lung nodule datasets.Cancer Imaging20242414010.1186/s40644‑024‑00683‑x38509635
     [Google Scholar]
  64. ZhouZ. GouF. TanY. WuJ. A cascaded multi-stage framework for automatic detection and segmentation of pulmonary nodules in developing countries.IEEE J. Biomed. Health Inform.202226115619563010.1109/JBHI.2022.319850935984795
     [Google Scholar]
  65. ArmatoS.G.III McLennanG. BidautL. McNitt-GrayM.F. MeyerC.R. ReevesA.P. ZhaoB. AberleD.R. HenschkeC.I. HoffmanE.A. KazerooniE.A. MacMahonH. van BeekE.J.R. YankelevitzD. BiancardiA.M. BlandP.H. BrownM.S. EngelmannR.M. LaderachG.E. MaxD. PaisR.C. QingD.P.Y. RobertsR.Y. SmithA.R. StarkeyA. BatraP. CaligiuriP. FarooqiA. GladishG.W. JudeC.M. MundenR.F. PetkovskaI. QuintL.E. SchwartzL.H. SundaramB. DoddL.E. FenimoreC. GurD. PetrickN. FreymannJ. KirbyJ. HughesB. Vande CasteeleA. GupteS. SallamM. HeathM.D. KuhnM.H. DharaiyaE. BurnsR. FrydD.S. SalganicoffM. AnandV. ShreterU. VastaghS. CroftB.Y. ClarkeL.P. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans.Med. Phys.201138291593110.1118/1.352820421452728
     [Google Scholar]
  66. JacobsC. van RikxoortE.M. MurphyK. ProkopM. Schaefer-ProkopC.M. van GinnekenB. Computer-aided detection of pulmonary nodules: A comparative study using the public LIDC/IDRI database.Eur. Radiol.20162672139214710.1007/s00330‑015‑4030‑726443601
     [Google Scholar]
  67. FedorovA. HancockM. ClunieD. BrochhausenM. BonaJ. KirbyJ. FreymannJ. PieperS. J W L AertsH. KikinisR. PriorF. DICOM re-encoding of volumetrically annotated Lung Imaging Database Consortium (LIDC) nodules.Med. Phys.202047115953596510.1002/mp.1444532772385
     [Google Scholar]
  68. SetioA.A.A. CiompiF. LitjensG. GerkeP. JacobsC. van RielS.J. WilleM.M.W. NaqibullahM. SánchezC.I. van GinnekenB. Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks.IEEE Trans. Med. Imaging20163551160116910.1109/TMI.2016.253680926955024
     [Google Scholar]
  69. TakahashiR. MatsubaraT. UeharaK. Data augmentation using random image cropping and patching for deep CNNs.IEEE Trans. Circ. Syst. Video Tech.20203092917293110.1109/TCSVT.2019.2935128
     [Google Scholar]
  70. HaoR. NamdarK. LiuL. HaiderM.A. KhalvatiF. A comprehensive study of data augmentation strategies for prostate cancer detection in diffusion-weighted MRI using convolutional neural networks.J. Digit. Imaging202134486287610.1007/s10278‑021‑00478‑734254200
     [Google Scholar]
  71. LyuJ. BiX. LingS.H. Multi-level cross residual network for lung nodule classification.Sensors20202010283710.3390/s2010283732429401
     [Google Scholar]
  72. ShaheedK. MaoA. QureshiI. KumarM. HussainS. UllahI. ZhangX. DS-CNN: A pre-trained Xception model based on depth-wise separable convolutional neural network for finger vein recognition.Expert Syst. Appl.202219111628810.1016/j.eswa.2021.116288
     [Google Scholar]
  73. ShahB. BhavsarH. Depth-restricted convolutional neural network - A model for Gujarati food image classification.Vis. Comput.20244031931194610.1007/s00371‑023‑02893‑z
     [Google Scholar]
  74. HeK. ZhangX. RenS. SunJ. Deep residual learning for image recognition.2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)201677077810.1109/CVPR.2016.90
     [Google Scholar]
  75. ZhangK. SunM. HanT.X. YuanX. GuoL. LiuT. Residual networks of residual networks: Multilevel residual networks.IEEE Trans. Circ. Syst. Video Tech.20182861303131410.1109/TCSVT.2017.2654543
     [Google Scholar]
  76. BoroumandM. ChenM. FridrichJ. Deep residual network for steganalysis of digital images.IEEE Trans. Inf. Forensics Security20191451181119310.1109/TIFS.2018.2871749
     [Google Scholar]
  77. ChattopadhayA. SarkarA. HowladerP. BalasubramanianV.N. Grad-CAM++: Generalized gradient-based visual explanations for deep convolutional networks.2018 IEEE Winter Conference on Applications of Computer Vision (WACV)IEEELake Tahoe, NV, USA201883984710.1109/WACV.2018.00097
     [Google Scholar]
  78. ZhangY. HongD. McClementD. OladosuO. PridhamG. SlaneyG. Grad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging.J. Neurosci. Methods202135310909810.1016/j.jneumeth.2021.10909833582174
     [Google Scholar]
  79. ChenY. ZhaoZ. YuY. WangW. TangC. Understanding IFRA for detecting synchronous machine winding short circuit faults based on image classification and smooth grad-CAM++.IEEE Sens. J.20232332422243210.1109/JSEN.2022.3225210
     [Google Scholar]
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A Novel Automatic Lung Nodule Classification Scheme using Fusion Ghost Convolution and Hybrid Normalization in Chest CTs
Curr. Med. Imaging 21, E15734056330120 (2025); https://doi.org/10.2174/0115734056330120250310053454
/content/journals/cmir/10.2174/0115734056330120250310053454
/content/journals/cmir/10.2174/0115734056330120250310053454
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  • Article Type:     Research Article
Keyword(s): Classification of pulmonary nodules; Computed tomography; Ghost convolution; Lung Nodule Analysis 16; Normalization; Visualization

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