Skip to content
2000
Volume 18, Issue 10
  • ISSN: 2352-0965
  • E-ISSN: 2352-0973

Abstract

Introduction

Malignant lung nodules are a major cause of lung cancer-related mortality and rank among the most prevalent cancers worldwide. Due to a lack of contrast, little nodules blend with their surroundings and other structures; therefore, it is difficult to detect them efficiently during the diagnostic phase, making it challenging for the radiologist to determine whether the nodule is malignant or not. This study evaluates the model’s performance in rapidly and accurately detecting nodules from lung CT scans.

Methods

Nodule detection is accomplished by using conventional diagnostic procedures such as radiographic imaging techniques and computerized tomography (CT). However, these methods aren't always effective in spotting tiny nodules, and they can put patients at risk of radiation exposure. Consequently, there has been a lot of research in this field using deep learning to process images and identify nodules in the lungs. To improve the model's accuracy, our method employs a residual bounding box-based optimized “You Only Look Once version 8x-Coordinates-To-Features” (YOLOv8x-C2f) model in conjunction with a handful of preprocessing steps.

Results

This model is evaluated with the help of the “Lung Image Database Consortium and Image Database Resource Initiative” LIDC/IDRI dataset, which was acquired through the lung nodule analysis (LUNA16) grand challenge. With an impressive mean average precision (mAP50) of 0.70% and precision of 89%, the suggested model achieves an impressive accuracy of 95.2% when it comes to nodule recognition with a confidence factor.

Conclusion

The study demonstrates that the model's superior architecture and features can accurately identify and localize nodules, enhancing overall performance relative to state-of-the-art approaches.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/raeeng/10.2174/0123520965340330250207072742
2025-05-02
2026-01-01

  • From This Site

    /content/journals/raeeng/10.2174/0123520965340330250207072742
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. GLOBOCAN. Factsheets, India 2022. https://gco.iarc.who.int/media/globocan/factsheets/populations/356-india-fact-sheet.pdf
  2. HansellD.M. BankierA.A. MacMahonH. McLoudT.C. MüllerN.L. RemyJ. Fleischner Society: Glossary of terms for thoracic imaging.Radiology2008246369772210.1148/radiol.2462070712 18195376
     [Google Scholar]
  3. 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.0210551 30629724
     [Google Scholar]
  4. ChiJ. ZhangS. YuX. WuC. JiangY. A novel pulmonary nodule detection model based on multi-step cascaded networks.Sensors20202015430110.3390/s20154301 32752225
     [Google Scholar]
  5. Lung cancer: Biology and treatment options.Biochim. Biophys. Acta201518562189210
     [Google Scholar]
  6. LudwigM. LudwigB. MikułaA. BiernatS. RudnickiJ. KaliszewskiK. The use of Artificial Intelligence in the diagnosis and classification of thyroid nodules: An update.Cancers202315370810.3390/cancers15030708 36765671
     [Google Scholar]
  7. 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.2018.10.011 30390571
     [Google Scholar]
  8. JafariH. FaezK. AmindavarH. Swin-Tempo: Temporal-aware lung nodule detection in CT scans as video sequences using swin transformer-enhanced UNetarXiv:2310.03365202310.48550/arXiv.2310.03365
     [Google Scholar]
  9. XiaoqiL. WuL. GuY. ZhangW.L. JingL.I. Detection of low dose CT pulmonary nodules based on 3D convolution neural network.Optics Precis Eng2018261211121810.3788/OPE.20182605.1211
     [Google Scholar]
  10. TiwariL. RajaR. AwasthiV. MiriR. SinhaG. AlkinaniM.H. PolatK. Detection of lung nodule and cancer using novel Mask-3 FCM and TWEDLNN algorithms.Measurement2020172
     [Google Scholar]
  11. HarsonoI.W. LiawatimenaS. CenggoroT.W. Lung nodule detection and classification from Thorax CT-scan using RetinaNet with transfer learning.J. King Saud Univ. Comput. Inf. Sci202234356757710.1016/j.jksuci.2020.03.013
     [Google Scholar]
  12. KanipriyaM. HemalathaC. SrideviN. SriVidhyaS.R. Jany ShabuS.L. An improved capuchin search algorithm optimized hybrid CNN-LSTM architecture for malignant lung nodule detectionBiomed. Signal Process. Control20227810397310.1016/j.bspc.2022.103973
     [Google Scholar]
  13. JournalI.R.J.E.T. YOLOv8-based lung nodule detection: A novel hybrid deep learning model proposal. vol. 10, 2023.IRJETvol. 10, 2023
     [Google Scholar]
  14. LiY. ZhengH. HuangX. ChangJ. HouD. LuH. Research on lung nodule recognition algorithm based on deep feature fusion and MKL-SVM-IPSO.Sci. Rep.20221211740310.1038/s41598‑022‑22442‑3 36257988
     [Google Scholar]
  15. RenC. HouS. HouJ. SwiF-YOLO: A deep learning method for lung nodule detection.J. Biol. Life Sci.202452202710.54097/rcx9h636
     [Google Scholar]
  16. HouF. SunJ. Lung-YOLO: Multiscale feature fusion attention and cross-layer aggregation for lung nodule detection.Biomed. Signal Process. Control202599SN-1746SN-8094
     [Google Scholar]
  17. ChauhanS. MalikN. VigR UNet with ResNextify and IB modules for low-dose CT image denoisingInt. J. Inf. Technol.2024164677469210.1007/s41870‑024‑01898‑8
     [Google Scholar]
  18. AldughayfiqB. AshfaqF. JhanjhiN.Z. HumayunM. YOLO-based deep learning model for pressure ulcer detection and classification.Healthcare2023119122210.3390/healthcare11091222 37174764
     [Google Scholar]
  19. DiwanT. AnirudhG. TembhurneJ.V. Object detection using YOLO: Challenges, architectural successors, datasets and applications.Multimedia Tools Appl.20238269243927510.1007/s11042‑022‑13644‑y 35968414
     [Google Scholar]
  20. YangL. CaiH. LuoX. WuJ. TangR. ChenY. LiW. A lightweight neural network for lung nodule detection based on improved ghost module.Quant. Imaging Med. Surg.20231374205422110.21037/qims‑21‑1182 37456313
     [Google Scholar]
  21. KimS. KimT.S. LeeW.H. Accelerating 3D convolutional neural network with channel bottleneck module for EEG-based emotion recognition.Sensors20222218681310.3390/s22186813 36146160
     [Google Scholar]
  22. ZhangH. PengY. GuoY. Pulmonary nodules detection based on multi-scale attention networks.Sci. Rep.2022121146610.1038/s41598‑022‑05372‑y
     [Google Scholar]
  23. Detection of low dose CT pulmonary nodules based on 3D CNNCapsNetAvailable from: https://www.authorea.com/users/625041/articles/647150-detection-of-low-dose-ct-pulmonary-nodules-based-on-3d-cnn-capsnet
  24. Prostate MR Image DatabaseAvailable from: https://prostatemrimagedatabase.com
  25. Akila AgnesS. Wavelet U-Net++ for accurate lung nodule segmentation in CT scans: Improving early detection and diagnosis of lung cancerBiomed. Signal Process. Control.202487Part A10550910.1016/j.bspc.2023.105509
     [Google Scholar]
  26. 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.201138291593110.1118/1.3528204 21452728
     [Google Scholar]
  27. PrasadRajesh JayashreeShardanand Prasad Gradient bald vulture optimization enabled multi-objective Unet++ with DCNN for prostate cancer segmentation and detectionBiomed. Signal Process. Control.2024Volume 87Part A10547410.1016/j.bspc.2023.105474
     [Google Scholar]
  28. Pulmonary nodule detection on lung parenchyma images using hyper-deep algorithm.Heliyon202397e17599
     [Google Scholar]
  29. WangY. SongX. GongG. LiN. A multi-scale feature extraction-based normalized attention neural network for image denoising.Electronics202110331910.3390/electronics10030319
     [Google Scholar]
  30. ZhouT. YeX. LuH. ZhengX. QiuS. LiuY. Dense convolutional network and its application in medical image analysis.BioMed Res. Int.2022202212210.1155/2022/2384830 35509707
     [Google Scholar]
  31. WangJ. ZhangW. FuJ. SongK. MengQ. ZhuL. JinY. LDCNet: A lightweight multiscale convolutional neural network using local dense connectivity for image recognition.IEEE Trans. Cogn. Dev. Syst.202416388889810.1109/TCDS.2023.3311171
     [Google Scholar]
  32. RedmonJ. DivvalaS. GirshickR. FarhadiA. You Only Look Once: Unified, Real-Time Object DetectionIEEE Conference on Computer Vision and Pattern Recognition (CVPR) Las Vegas, NV, USA, 2016, pp. 779-78810.1109/CVPR.2016.91
     [Google Scholar]
  33. VargheseR. YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS) Chennai, India, 18-19 April 2024, pp. 1-610.1109/ADICS58448.2024.10533619
     [Google Scholar]
  34. WangJ. YangP. LiuY. ShangD. HuiX. SongJ. ChenX. Research on Improved YOLOv5 for Low-Light Environment Object Detection.Electronics20231214308910.3390/electronics12143089
     [Google Scholar]
  35. TranGiang Son Improving accuracy of lung nodule classification using deep learning with focal loss.J. Healthc. Eng.19955156416202910.1155/2019/5156416 30863524
     [Google Scholar]
  36. HeK. ZhangX. RenS. SunJ. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. Computer Vision – ECCV 2014. ECCV Lecture Notes in Computer Science. FleetD. PajdlaT. SchieleB. Tuytelaarsand T. ChamSpringer2014Vol. 869110.1007/978‑3‑319‑10578‑9_23
     [Google Scholar]
  37. YuH. LiJ. ZhangL. CaoY. YuX. SunJ. Design of lung nodules segmentation and recognition algorithm based on deep learningBMC Bioinformatics202122S5, suppl. Suppl. 531410.1186/s12859‑021‑04234‑034749636
     [Google Scholar]
  38. LiangH. XianA. MaoM. NiP. WuH. A research on remote fracturing monitoring and decision-making method supporting smart city.Sustain Cities Soc.20206210241410.1016/j.scs.2020.102414
     [Google Scholar]
  39. LiX. LvC. WangW. LiG. YangL. YangJ. Generalized focal loss: Towards efficient representation learning for dense object detection.IEEE Trans. Pattern Anal. Mach. Intell.202345331393153 35679384
     [Google Scholar]
  40. WangA. LiangG. WangX. SongY. Application of the YOLOv6 combining CBAM and CIoU in forest fire and smoke detection.Forests20231411226110.3390/f14112261
     [Google Scholar]
  41. WuZ. ZhouQ. WangF. Coarse-to-fine lung nodule segmentation in CT images with image enhancement and dual-branch network.IEEE Access202197255726210.1109/ACCESS.2021.3049379
     [Google Scholar]
  42. JiZ. WuY. ZengX. AnY. ZhaoL. WangZ. GanchevI. Lung nodule detection in medical images based on improved YOLOv5s.IEEE Access202311763717638710.1109/ACCESS.2023.3296530
     [Google Scholar]
  43. JiZ. ZhaoJ. LiuJ. ZengX. ZhangH. ZhangX. GanchevI. ELCT-YOLO: An efficient one-stage model for automatic lung tumor detection based on CT images.Mathematics20231110234410.3390/math11102344
     [Google Scholar]
  44. LiuChenyang HuShen-Chiang Automatic detection of pulmonary nodules on CT images with YOLOv3: Development and evaluation using simulated and patient data.Quant. Imaging Med. Surg.2020101019171929
     [Google Scholar]
  45. AlakwaaW. NassefM. BadrA. Lung cancer detection and classification with 3D convolutional neural network (3D-CNN).Int. J. Adv. Comput. Sci. Appl.201788810.14569/IJACSA.2017.080853
     [Google Scholar]
  46. YeY. TianM. LiuQ. TaiH-M. Pulmonary nodule detection using V-net and high-level descriptor based SVM classifier.IEEE Access2020817603317604110.1109/ACCESS.2020.3026168
     [Google Scholar]
  47. HuangX. SunW. TsengT.L.B. LiC. QianW. Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networksComput. Med. Imaging Graph.2019742536, 202910.1016/j.compmedimag.2019.02.00330954678
     [Google Scholar]
  48. HanY. QiH. WangL. ChenC. MiaoJ. XuH. WangZ. GuoZ. XuQ. LinQ. LiuH. LuJ. LiangF. FengW. LiH. LiuY. Pulmonary nodules detection assistant platform: An effective computer aided system for early pulmonary nodules detection in physical examination.Comput. Methods Programs Biomed.202221710668010.1016/j.cmpb.2022.106680 35176595
     [Google Scholar]
  49. WuXiaosheng ZhangHang YOLO-MSRF for lung nodule detectionBiol. Signal Process. Control.1063189410.1016/j.bspc.2024.106318
     [Google Scholar]
/content/journals/raeeng/10.2174/0123520965340330250207072742
Loading
Pulmonary Nodule Detection Using RBB-Based Optimised Yolov8x-C2f Network
Recent Advances in Electrical & Electronic Engineering 18, 1981 (2025); https://doi.org/10.2174/0123520965340330250207072742
/content/journals/raeeng/10.2174/0123520965340330250207072742
/content/journals/raeeng/10.2174/0123520965340330250207072742
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): CNN; CT images; deep learning; Lung nodules; YOLO; YOLOv8x

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test