Skip to content
2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
file format pdf download PDF
side by side viewer icon HTML

Abstract

Background:

The most difficult aspect of diagnosing lung cancer is early diagnosis. According to the American Cancer Society, each year, there are around 11 million newly diagnosed instances of cancer worldwide. Radiologists often turn to Computed Tomography (CT) scans to diagnose respiratory conditions, which can reveal if lung tissue remains normal or abnormal. However, there is an increased chance of inaccuracy and delay; therefore, radiologists are concerned with the physical segmentation of nodules.

Objective:

The objective of the research is to implement an advanced modified threshold segmentation and classification model for early and accurate detection of lung cancer from CT images.

Methods:

Using the Support Vector Machines (SVM) classifier as well as the Artificial Neural Network (ANN) classifier, the authors propose using Modified adaptive threshold segmentation as a segmentation approach for cancer detection. Here, Lung Image Database Consortium (LIDC) datasets, a collection of CT scans, are used as the video frames in an investigation to authorize the recitation of the suggested technique.

Results:

Both quantitative as well as qualitative analyses are used to analyze the segmentation function of the anticipated algorithm. Both the ANN and SVM classifiers used in the suggested technique for lung cancer diagnosis achieve world-record levels of accuracy, with the former achieving a 96.3% detection rate and the latter a 97% rate of accuracy.

Conclusion:

This innovation may have a major impact on the worldwide rate of lung cancer rate due to its ability to detect lung tumors in their earliest stages when they are most amenable to being avoided and treated. This method is useful because it provides more information and facilitates quick, precise decision-making for doctors diagnosing lung cancer in their patients.

© 2024 The Author(s). Published by Bentham Science Publisher.
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.
Loading

Article metrics loading...

/content/journals/cmir/10.2174/1573405620666230714110914
2023-12-18
2025-09-11

  • From This Site

    /content/journals/cmir/10.2174/1573405620666230714110914
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

/deliver/fulltext/cmir/20/1/e140723218727.html?itemId=/content/journals/cmir/10.2174/1573405620666230714110914&mimeType=html&fmt=ahah

References

  1. SocietyC. Cancer facts and figures.American Cancer SocietyAtlanta2013
     [Google Scholar]
  2. Cancer Facts and Figures2017Available from: https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2017.html
  3. HansellD.M. BankierA.A. MacMahonH. McLoudT.C. MüllerN.L. RemyJ. Fleischner Society: Glossary of terms for thoracic imaging.Radiology2008246369772210.1148/radiol.246207071218195376
     [Google Scholar]
  4. ValenteI.R.S. CortezP.C. NetoE.C. SoaresJ.M. de AlbuquerqueV.H.C. TavaresJ.M.R.S. Automatic 3D pulmonary nodule detection in CT images: A survey.Comput. Methods Programs Biomed.20161249110710.1016/j.cmpb.2015.10.00626652979
     [Google Scholar]
  5. LeeS.L.A. KouzaniA.Z. HuE.J. Automated detection of lung nodules in computed tomography images: A review.Mach. Vis. Appl.201223115116310.1007/s00138‑010‑0271‑2
     [Google Scholar]
  6. ArmatoS.G.III MeyerC.R. McNitt-GrayM.F. McLennanG. ReevesA.P. CroftB.Y. ClarkeL.P. The Reference Image Database to Evaluate Response to therapy in lung cancer (RIDER) project: A resource for the development of change-analysis software.Clin. Pharmacol. Ther.200884444845610.1038/clpt.2008.16118754000
     [Google Scholar]
  7. McNitt-GrayM.F. ArmatoS.G.III MeyerC.R. ReevesA.P. McLennanG. PaisR.C. The LIDC(LIDC) data collection process for nodule detection and annotation.Acad. Radiol.200714121464147410.1016/j.acra.2007.07.02118035276
     [Google Scholar]
  8. Public Lung Image DatabaseAvailable from: http://www.via.cornell.edu/lungdb.html
  9. CascioD. MagroR. FauciF. IacomiM. RasoG. Automatic detection of lung nodules in CT datasets based on stable 3D mass–spring models.Comput. Biol. Med.201242111098110910.1016/j.compbiomed.2012.09.00223020972
     [Google Scholar]
  10. SoltaninejadS. KeshaniM. TajeripourF. Lung nodule detection by KNN classifier and active contour modelling and 3D visualization.The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012)2012440445
     [Google Scholar]
  11. KimH. NakashimaT. ItaiY. MaedaS. Automatic detection of ground glass opacity from the thoracic MDCT images by using density features.2007 International Conference on Control, Automation and Systems200712741277
     [Google Scholar]
  12. PuJ. RoosJ. YiC.A. NapelS. RubinG.D. PaikD.S. Adaptive border marching algorithm: Automatic lung segmentation on chest CT images.Comput. Med. Imaging Graph.200832645246210.1016/j.compmedimag.2008.04.00518515044
     [Google Scholar]
  13. GoriI. BellottiR. CerelloP. CheranS.C. De NunzioG. FantacciM.E. Lung nodule detection in screening computed tomography.2006 IEEE Nuclear Science Symposium Conference Record2006634893491
     [Google Scholar]
  14. WeiG.Q. FanL. QianJ. Automatic detection of nodules attached to vessels in lung CT by volume projection analysis.International Conference on Medical Image Computing and Computer-Assisted InterventionSpringerBerlin, Heidelberg200274675210.1007/3‑540‑45786‑0_92
     [Google Scholar]
  15. ReticoA. DeloguP. FantacciM.E. GoriI. Preite MartinezA. Lung nodule detection in low-dose and thin-slice computed tomography.Comput. Biol. Med.200838452553410.1016/j.compbiomed.2008.02.00118342844
     [Google Scholar]
  16. NaminS.T. MoghaddamH.A. JafariR. Esmaeil-ZadehM. GityM. Automated detection and classification of pulmonary nodules in 3D thoracic CT images.2010 IEEE International Conference on Systems, Man and Cybernetics20103774377910.1109/ICSMC.2010.5641820
     [Google Scholar]
  17. ChoiW.J. ChoiT.S. Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor.Comput. Methods Programs Biomed.20141131375410.1016/j.cmpb.2013.08.01524148147
     [Google Scholar]
  18. KeshaniM. AzimifarZ. TajeripourF. BoostaniR. Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system.Comput. Biol. Med.201343428730010.1016/j.compbiomed.2012.12.00423369568
     [Google Scholar]
  19. KimD.Y. KimJ.H. NohS.M. ParkJ.W. Pulmonary nodule detection using chest CT images.Acta Radiol.200344325225710.1080/j.1600‑0455.2003.00061.x12751994
     [Google Scholar]
  20. BellottiR. De CarloF. GarganoG. TangaroS. CascioD. CatanzaritiE. CerelloP. CheranS.C. DeloguP. De MitriI. FulcheriC. GrossoD. ReticoA. SquarciaS. TommasiE. GolosioB. A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model.Med. Phys.200734124901491010.1118/1.280472018196815
     [Google Scholar]
  21. El-BazA. ElnakibA. Abou El-GharM. Gimel’farbG. FalkR. FaragA. Automatic detection of 2D and 3D lung nodules in chest spiral CT scans.Int. J. Biomed. Imaging2013201311110.1155/2013/51763223509444
     [Google Scholar]
  22. SuiyuanW. JunfengW. Pulmonary nodules 3D detection on serial CT scans.2012 Third Global Congress on Intelligent Systems201225726010.1109/GCIS.2012.46
     [Google Scholar]
  23. XuN. AhujaN. BansalR. Automated lung nodule segmentation using dynamic programming and EM-based classification.Medical Imaging 2002: Image Processing. International Society for Optics and Photonics20024684666676
     [Google Scholar]
  24. AoyamaM. LiQ. KatsuragawaS. LiF. SoneS. DoiK. Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low-dose CT images.Med. Phys.200330338739410.1118/1.154357512674239
     [Google Scholar]
  25. WangJ. EngelmannR. LiQ. Segmentation of pulmonary nodules in three-dimensional CT images by use of a spiral-scanning technique.Med. Phys.200734124678468910.1118/1.279988518196795
     [Google Scholar]
  26. FanL. QianJ. OdryB.L. ShenH. NaidichD. KohlG. KlotzE. Automatic segmentation of pulmonary nodules by using dynamic 3D cross-correlation for interactive CAD systems.Medical Imaging 2002: Image Processing. International Society for Optics and Photonics2002468413621369
     [Google Scholar]
  27. KostisW.J. ReevesA.P. YankelevitzD.F. HenschkeC.I. Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images.IEEE Trans. Med. Imaging200322101259127410.1109/TMI.2003.81778514552580
     [Google Scholar]
  28. EnquobahrieA.A. ReevesA.P. YankelevitzD.F. HenschkeC.I. Automated detection of pulmonary nodules from whole lung helical CT scans: Performance comparison for isolated and attached nodules.Medical Imaging 2004: Image Processing. International Society for Optics and Photonics20045370791800
     [Google Scholar]
  29. LiQ. LiF. DoiK. Computerized detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated rule-based classifier.Acad. Radiol.200815216517510.1016/j.acra.2007.09.01818206615
     [Google Scholar]
  30. KawataY. NikiN. OhmatsuH. KusumotoM. KakinumaR. MoriK. Hybrid classification approach of malignant and benign pulmonary nodules based on topological and histogram features.International Conference on Medical Image Computing and Computer-Assisted Intervention200029730610.1007/978‑3‑540‑40899‑4_30
     [Google Scholar]
  31. MatsumotoS. KundelH.L. GeeJ.C. GefterW.B. HatabuH. Pulmonary nodule detection in CT images with quantized convergence index filter.Med. Image Anal.200610334335210.1016/j.media.2005.07.00116542867
     [Google Scholar]
  32. JiaT. ZhaoD.Z. YangJ.Z. WangX. Automated detection of pulmonary nodules in HRCT images.2007 1st International Conference on Bioinformatics and Biomedical Engineering2007833836
     [Google Scholar]
  33. FukanoG. TakizawaH. ShigemotoK. YamamotoS. MatsumotoT. TatenoY. IinumaT. Recognition method of lung nodules using blood vessel extraction techniques and 3D object models.Medical Imaging 2003: Image Processing International Society for Optics and Photonics20035032190198
     [Google Scholar]
  34. ZhaoL. BoroczkyL. LeeK.P. False positive reduction for lung nodule CAD using support vector machines and genetic algorithms.International Congress Series200512811109111410.1016/j.ics.2005.03.061
     [Google Scholar]
  35. DehmeshkiJ. ChenJ. CasiqueM.V. KarakoyM. Classification of lung data by sampling and support vector machine.Conf Proc IEEE Eng Med Biol Soc2004200431943197
     [Google Scholar]
  36. SantosA.M. de Carvalho FilhoA.O. SilvaA.C. de PaivaA.C. NunesR.A. GattassM. Automatic detection of small lung nodules in 3D CT data using Gaussian mixture models, Tsallis entropy and SVM.Eng. Appl. Artif. Intell.201436273910.1016/j.engappai.2014.07.007
     [Google Scholar]
  37. MatsumotoS. OhnoY. YamagataH. TakenakaD. SugimuraK. Computer-aided detection of lung nodules on multidetector row computed tomography using three-dimensional analysis of nodule candidates and their surroundings.Radiat. Med.200826956256910.1007/s11604‑008‑0272‑519030967
     [Google Scholar]
  38. Al-TarawnehM.S. Lung cancer detection using image processing techniques.Leonardo J. Pract. Technol.20121120147158
     [Google Scholar]
  39. ChaudharyA. SinghS.S. Lung cancer detection on CT images by using image processing.2012 International Conference on Computing Sciences201214214610.1109/ICCS.2012.43
     [Google Scholar]
  40. HadaviN. NordinM.J. ShojaeipourA. Lung cancer diagnosis using CT-scan images based on cellular learning automata.2014 International Conference on Computer and Information Sciences (ICCOINS)20141510.1109/ICCOINS.2014.6868370
     [Google Scholar]
  41. AggarwalT. FurqanA. KalraK. Feature extraction and LDA based classification of lung nodules in chest CT scan images.2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)20151189119310.1109/ICACCI.2015.7275773
     [Google Scholar]
  42. BhatG. BiradarV. G. SarojadeviH. Artificial Neural Network based Cancer Cell Classification (ANN–C3).2012
     [Google Scholar]
  43. PathanA. SaptalkarB.K. Detection and classification of lung cancer using artificial neural network.International Journal on Advanced Computer Engineering and Communication Technology201211
     [Google Scholar]
  44. LinD.T. YanC.R. Lung nodules identification rules extraction with neural fuzzy network.Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP'022002420492053
     [Google Scholar]
  45. ZhaoB. GamsuG. GinsbergM. S. JiangL. SchwartzL. H. Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm.J Appl Clin Med Phys20034324860
     [Google Scholar]
  46. El-BazlA. FaragA.A. FalkR. La RoccaR. Automatic identification of lung abnormalities in chest spiral CT scans.2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings (ICASSP'03)20032261
     [Google Scholar]
  47. Van GinnekenB. Ter Haar RomenyB.M. ViergeverM.A. Computer-aided diagnosis in chest radiography: A survey.IEEE Trans. Med. Imaging200120121228124110.1109/42.97491811811823
     [Google Scholar]
  48. JoonP. JatainA. BhaskarS.B. Lung cancer detection using image processing techniques: Review.Int J Eng Sci Sci & Comp.201774
     [Google Scholar]
  49. SharmaD. JindalG. Identifying lung cancer using image processing techniques.International Conference on Computational Techniques and Artificial Intelligence (ICCTAI)201117872880
     [Google Scholar]
  50. ullahM. BariM. AhmedA. NaveedS. Lung cancer detection using digital image processing techniques: A review.Mehran Univ. Res. J. Eng. Technol.201938235136010.22581/muet1982.1902.10
     [Google Scholar]
  51. AdaR.K. Early detection and prediction of lung cancer survival using neural network classifier.Int. J. Appl. Innov. Eng. Manag.201326
     [Google Scholar]
  52. MasudM. SikderN. NahidA.A. BairagiA.K. AlZainM.A. A machine learning approach to diagnosing lung and colon cancer using a deep learning-based classification framework.Sensors202121374810.3390/s2103074833499364
     [Google Scholar]
  53. ZhengQ. YangL. ZengB. LiJ. GuoK. LiangY. LiaoG. Artificial intelligence performance in detecting tumor metastasis from medical radiology imaging: A systematic review and meta-analysis.E Clinical Medicine20213110066910.1016/j.eclinm.2020.10066933392486
     [Google Scholar]
  54. MalekiN. ZeinaliY. NiakiS.T.A. A k-NN method for lung cancer prognosis with the use of a genetic algorithm for feature selection.Expert Syst. Appl.202116411398110.1016/j.eswa.2020.113981
     [Google Scholar]
  55. ItoH. SuzukiK. MizutaniT. AokageK. WakabayashiM. FukudaH. WatanabeS. ItoH. SuzukiK. MizutaniT. AokageK. WakabayashiM. KoikeT. TsutaniY. SajiH. NakagawaK. ZenkeY. TakamochiK. AokiT. OkamiJ. YoshiokaH. ShionoS. OkadaM. WatanabeS. Long-term survival outcome after lobectomy in patients with clinical T1 N0 lung cancer.J. Thorac. Cardiovasc. Surg.2021161128129010.1016/j.jtcvs.2019.12.07232067786
     [Google Scholar]
  56. NingZ. LuoJ. LiY. HanS. FengQ. XuY. Pattern classification for gastrointestinal stromal tumors by integration of radiomics and deep convolutional features.IEEE J. Biomed. Health Inform.201923311819129993591
     [Google Scholar]
  57. ChoiY.S. BaeS. ChangJ.H. KangS.G. KimS.H. KimJ. RimT.H. ChoiS.H. JainR. LeeS.K. Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics.Neuro-oncol.202123230431310.1093/neuonc/noaa17732706862
     [Google Scholar]
  58. CalabreseE. RudieJ.D. RauscheckerA.M. Villanueva-MeyerJ.E. ClarkeJ.L. SolomonD.A. ChaS. Combining radiomics and deep convolutional neural network features from preoperative MRI for predicting clinically relevant genetic biomarkers in glioblastoma.Neurooncol. Adv.202241vdac06010.1093/noajnl/vdac06035611269
     [Google Scholar]
  59. HuangW. WangJ. WangH. ZhangY. ZhaoF. LiK. SuL. KangF. CaoX. PET/CT based EGFR mutation status classification of NSCLC using deep learning features and radiomics features.Front. Pharmacol.20221389852910.3389/fphar.2022.89852935571081
     [Google Scholar]
  60. VuongT.T.L. SongB. KimK. ChoY.M. KwakJ.T. Multi-scale binary pattern encoding network for cancer classification in pathology images.IEEE J. Biomed. Health Inform.20222631152116310.1109/JBHI.2021.309981734310334
     [Google Scholar]
  61. NingZ. TuC. XiaoQ. LuoJ. ZhangY. Multi-scale gradational-order fusion framework for breast lesions classification using ultrasound images.Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020202023Part VI171180
     [Google Scholar]
  62. WangL. ZhouX. NieX. LinX. LiJ. ZhengH. XueE. ChenS. ChenC. DuM. TongT. GaoQ. ZhengM. A multi-scale densely connected convolutional neural network for automated thyroid nodule classification.Front. Neurosci.20221687871810.3389/fnins.2022.87871835663553
     [Google Scholar]
  63. CheH. BrownL.G. ForanD.J. NosherJ.L. HacihalilogluI. Liver disease classification from ultrasound using multi-scale CNN.Int. J. CARS20211691537154810.1007/s11548‑021‑02414‑034097226
     [Google Scholar]
  64. DouQ. ChenH. JinY. LinH. QinJ. HengP.A. Automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid-loss residual learning.Medical Image Computing and Computer Assisted Intervention− MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III 202017630638Springer International Publishing.
     [Google Scholar]
  65. ChuiK.T. GuptaB.B. JhaveriR.H. ChiH.R. AryaV. AlmomaniA. NaumanA. Multiround Transfer Learning and Modified Generative Adversarial Network for Lung Cancer Detection.Int. J. Intell. Syst.2023202311410.1155/2023/6376275
     [Google Scholar]
  66. ChanM.V. HuoY.R. CaoC. RidleyL. Survival outcomes for surgical resection versus CT-guided percutaneous ablation for stage I non-small cell lung cancer (NSCLC): A systematic review and meta-analysis.Eur. Radiol.20213175421543310.1007/s00330‑020‑07634‑733449192
     [Google Scholar]
  67. NaikN. HameedB.M.Z. ShettyD.K. SwainD. ShahM. PaulR. AggarwalK. IbrahimS. PatilV. SmritiK. ShettyS. RaiB.P. ChlostaP. SomaniB.K. Legal and ethical consideration in artificial intelligence in healthcare: Who takes responsibility?Front. Surg.2022986232210.3389/fsurg.2022.86232235360424
     [Google Scholar]
  68. IqbalM.J. JavedZ. SadiaH. QureshiI.A. IrshadA. AhmedR. MalikK. RazaS. AbbasA. PezzaniR. Sharifi-RadJ. Clinical applications of artificial intelligence and machine learning in cancer diagnosis: Looking into the future.Cancer Cell Int.202121127010.1186/s12935‑021‑01981‑134020642
     [Google Scholar]
  69. IencaM. IgnatiadisK. Artificial intelligence in clinical neuroscience: methodological and ethical challenges.AJOB Neurosci.2020112778710.1080/21507740.2020.174035232228387
     [Google Scholar]
  70. HigginsO. ShortB.L. ChalupS.K. WilsonR.L. Artificial intelligence ( AI ) and machine learning ( ML ) based decision support systems in mental health: An integrative review.Int. J. Ment. Health Nurs.2023inm.1311410.1111/inm.1311436744684
     [Google Scholar]
  71. ShreveJ.T. KhananiS.A. HaddadT.C. Artificial intelligence in oncology: Current capabilities, future opportunities, and ethical considerations.Am. Soc. Clin. Oncol. Educ. Book2022424284285110.1200/EDBK_35065235687826
     [Google Scholar]
/content/journals/cmir/10.2174/1573405620666230714110914
Loading
Lung Cancer Detection from CT Images: Modified Adaptive Threshold Segmentation with Support Vector Machines and Artificial Neural Network Classifier
Curr. Med. Imaging 20, e140723218727 (2024); https://doi.org/10.2174/1573405620666230714110914
/content/journals/cmir/10.2174/1573405620666230714110914
/content/journals/cmir/10.2174/1573405620666230714110914
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): Accuracy; Classifier; Computed tomography; Diagnosis; Lidc dataset; Lung cancer

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