Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Recent Patents on Engineering
  4. Volume 19, Issue 2
  5. Article
Volume 19, Issue 2
  • ISSN: 1872-2121
  • E-ISSN: 2212-4047

Abstract

Background

The main objective of the Internet of Things (IoT) has significantly influenced and altered technology, such as interconnection, interoperability, and sensor devices. To ensure seamless healthcare facilities, it's essential to use the benefits of ubiquitous IoT services to assist patients by monitoring vital signs and automating functions. In healthcare, the current state-of-the-art equipment cannot detect many cancers early, and almost all humans have lost their lives due to this lethal sickness. Hence, early diagnosis of cancer is a significant difficulty for medical experts and researchers.

Methods

The method for identifying cancer, together with machine learning and IoT, yield reliable results. In the Proposed model FCM system, the SVM methodology is reviewed to classify either benign or malignant disease. In addition, we applied a recursive feature selection to identify characteristics from the cancer dataset to boost the classifier system's capabilities.

Results

This method is being applied in conjunction with fuzzy cluster-based augmentation, and classification can employ continuous monitoring to forecast lung cancer to improve patient care. In the process of effective image segmentation, the fuzzy-clustering methodology is implemented, which is used for the goal of obtaining transition region data.

Conclusion

The Otsu thresholding method is applied to help recover the transition region from a lung cancer image. Furthermore, morphological thinning on the right edge and the segmentation-improving pictures are employed to increase segmentation performance. In future work, we intend to design a prototype to ensure real-time analysis to provide enhanced results. Thus, this work may open doors to carry patent-based outcomes.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/eng/10.2174/0118722121248136230928053214
2025-02-01
2024-11-22

  • From This Site

    /content/journals/eng/10.2174/0118722121248136230928053214
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. HamdiM. M. MustafaA. S. MahdH. F. AboodM. S. KumarC. Al-shareedaM. A. Performance Analysis of QoS in MANET based on IEEE 802.11 b.2020 IEEE international conference for innovation in technology., 2020 06-08 November 2020, Bangluru, India, pp. 1-5, 2020.
     [Google Scholar]
  2. Al-ShareedaM.A. ManickamS. Man-in-the-middle attacks in mobile ad hoc networks (MANETs): Analysis and evaluation.Symmetry2022148154310.3390/sym14081543
     [Google Scholar]
  3. Al-MekhlafiZ.G. Al-ShareedaM.A. ManickamS. MohammedB.A. QtaishA. Lattice-based lightweight quantum resistant scheme in 5g-enabled vehicular networks.Mathematics202311239910.3390/math11020399
     [Google Scholar]
  4. Al-MekhlafiZ.G. Al-ShareedaM.A. ManickamS. MohammedB.A. AlreshidiA. AlazmiM. AlshudukhiJ.S. AlsaffarM. AlsewariA. Chebyshev polynomial-based fog computing scheme supporting pseudonym revocation for 5g-enabled vehicular networks.Electronics202312487210.3390/electronics12040872
     [Google Scholar]
  5. Al-ShareedaM.A. ManickamS. COVID-19 vehicle based on an efficient mutual authentication scheme for 5g-enabled vehicular fog computing.Int. J. Environ. Res. Public. Health202219231561810.3390/ijerph19231561836497709
     [Google Scholar]
  6. ProkoskiF.J. System and method for using three dimensional infrared imaging to provide detailed anatomical structure maps., 2013. Patent US8463006
     [Google Scholar]
  7. SharmaN. AggarwalL. Automated medical image segmentation techniques.J. Med. Phys.2010351314
     [Google Scholar]
  8. FukushimaK. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position.BiologicalCybernetic1980364193202
     [Google Scholar]
  9. SinghC. BalaA. A DCT-based local and non-local fuzzy C-means algorithm for segmentation of brain magnetic resonance images.Appl. Soft Comput.20186844745710.1016/j.asoc.2018.03.054
     [Google Scholar]
  10. SalunkeP. NerkarR. IoT driven healthcare system for remote monitoring of patients.Int. j. mod. trends sci. technol.201736100103
     [Google Scholar]
  11. JebaduraiJ. Dinesh PeterJ. Super-resolution of retinal images using multi-kernel SVR for IoT healthcare applications.Future Gener. Comput. Syst.20188333834610.1016/j.future.2018.01.058
     [Google Scholar]
  12. RastiR. TeshnehlabM. PhungS.L. Breast cancer diagnosis in DCE-MRI using mixture ensemble of convolutional neural networks.Pattern Recognit.2017722438139010.1016/j.patcog.2017.08.004
     [Google Scholar]
  13. YangS.C. A robust approach for subject segmentation of medical Images: Illustration with mammograms and breast magnetic resonance images.Comput. Electr. Eng.20176215116510.1016/j.compeleceng.2016.12.022
     [Google Scholar]
  14. ManikandanS. RamarK. WilljuiceIruthayarajanM. SrinivasaganK.G. Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm.Measurement201447558568
     [Google Scholar]
  15. KannanS.R. SathyaA. RamathilagamS. DeviR. Novel segmentation algorithm in segmenting medical images.J. Syst. Softw.201083122487249510.1016/j.jss.2010.07.036
     [Google Scholar]
  16. ChenH.L. YangB. LiuJ. LiuD.Y. A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis.Expert Syst. Appl.20113879014902210.1016/j.eswa.2011.01.120
     [Google Scholar]
  17. XiaoY. WuJ. LinZ. ZhaoX. Breast cancer diagnosis using an unsupervised feature extraction algorithm based on deep learning.Proceedings of the 2018 37th Chinese Control Conference (CCC), 2018 Wuhan, China 25-27 July 2018, Wuhan, China, pp.9428,9433.10.23919/ChiCC.2018.8483140
     [Google Scholar]
  18. KalshettiP. BundeleM. RahangdaleP. JangraD. ChattopadhyayC. HaritG. ElhenceA. An interactive medical image segmentation framework using iterative refinement.Comput. Biol. Med.201783223310.1016/j.compbiomed.2017.02.00228214717
     [Google Scholar]
  19. ZhouS. WangJ. ZhangM. CaiQ. GongY. Correntropy-based level set method for medical image segmentation and bias correction.Neurocomputing.20172341921622910.1016/j.neucom.2017.01.013
     [Google Scholar]
  20. ChenY.T. A novel approach to segmentation and measurement of medical image using level set methods.Magn. Reson. Imaging20173917519310.1016/j.mri.2017.02.00828219649
     [Google Scholar]
  21. BaiP.R. LiuQ.Y. LiL. TengS.H. LiJ. CaoM.Y. CaoY. A novel region-based level set method initialized with mean shift clustering for automated medical image segmentation.Comput. Biol. Med.201343111827183210.1016/j.compbiomed.2013.08.02424209928
     [Google Scholar]
  22. Khanfir KallelI. AlmouahedS. SolaimanB. BosséÉ. An iterative possibilistic knowledge diffusion approach for blind medical image segmentation.Pattern Recognit.20187818219710.1016/j.patcog.2018.01.024
     [Google Scholar]
  23. ZhengQ. LiH. FanB. WuS. XuJ. Integrating support vector machine and graph cuts for medical image segmentation.J. Vis. Commun. Image Represent.20185515716510.1016/j.jvcir.2018.06.005
     [Google Scholar]
  24. LiuC. NgM.K.P. ZengT. Weighted variational model for selective image segmentation with application to medical images.Pattern Recognit.20187636737910.1016/j.patcog.2017.11.019
     [Google Scholar]
  25. DrozdzalM. ChartrandG. VorontsovE. ShakeriM. Di JorioL. TangA. RomeroA. BengioY. PalC. KadouryS. Learning normalized inputs for iterative estimation in medical image segmentation.Med. Image Anal.20184411310.1016/j.media.2017.11.00529169029
     [Google Scholar]
  26. ZhaoW. XuX. ZhuY. XuF. Active contour model based on local and global Gaussian fitting energy for medical image segmentation.Optik.20181581160116910.1016/j.ijleo.2018.01.004
     [Google Scholar]
  27. VardhanaM. ArunkumarN. LasradoS. AbdulhayE. Ramirez-GonzalezG. Convolutional neural network for bio-medical image segmentation with hardware acceleration.Cogn. Syst. Res.201850101410.1016/j.cogsys.2018.03.005
     [Google Scholar]
  28. MiaoJ. HuangT.Z. ZhouX. WangY. LiuJ. Image segmentation based on an active contour model of partial image restoration with local cosine fitting energy.Inf. Sci.2018447527110.1016/j.ins.2018.02.007
     [Google Scholar]
  29. YuehongY.I.N. ZengY. ChenX. FanY. The internet of things in healthcare: An overview.J. Ind. Inf. Integr.20161313
     [Google Scholar]
  30. ChowE.J. DoodyD.R. DiC. ArmenianS.H. BakerK.S. BrickerJ.B. MendozaJ.A. Feasibility of a behavioral intervention using mobile health applications to reduce cardiovascular risk factors in cancer survivors: A pilot randomized controlled trial.J. Cancer Surviv.201011033037989
     [Google Scholar]
  31. SouriA. GhafourM.Y. AhmedA.M. SafaraF. YaminiA. HoseyninezhadM. A new machine learning-based healthcare monitoring model for student’s condition diagnosis in Internet of Things environment.Soft Comput.20202422171111712110.1007/s00500‑020‑05003‑6
     [Google Scholar]
  32. LiS. Da XuL. ZhaoS. 5G internet of things: A survey.J. Ind. Inf. Integr.20181019
     [Google Scholar]
  33. ViriyasitavatW. AnuphaptrirongT. HoonsoponD. When blockchain meets internet of things: Characteristics, challenges, and business opportunities.J. Ind. Inf. Integr.201915212810.1016/j.jii.2019.05.002
     [Google Scholar]
  34. SmitiA. When machine learning meets medical world: Current status and future challenges.Comput. Sci. Rev.20203710028010.1016/j.cosrev.2020.100280
     [Google Scholar]
  35. FouadH. HassaneinA. S. SolimanA. M. Al-FeelH. Analyzing patient health information based on IoT sensor with AI for improving patient assistance in the future direction.Measurement2020159107757
     [Google Scholar]
  36. UddinS. KhanA. HossainM.E. MoniM.A. Comparing different supervised machine learning algorithms for disease prediction.BMC Med. Inform. Decis. Mak.201919128110.1186/s12911‑019‑1004‑831864346
     [Google Scholar]
  37. VijayaK. PrathushaL. Smart wearable sensor design techniques for mobile health care solutions.Mobile computing solutions for healthcare systems2023102174204222
     [Google Scholar]
  38. SahithiG. VaddiR. ManneS. GuntiS. SatagopamS.K. Digitization of prior authorization in healthcare management using machine learning.Curr. Signal Transduct. Ther.2022173
     [Google Scholar]
  39. PetousisP. WinterA. SpeierW. AberleD.R. HsuW. BuiA.A.T. Using sequential decision making to improve lung cancer screening performance.IEEE Access2019711940311941910.1109/ACCESS.2019.293576332754420
     [Google Scholar]
  40. GobinathC. GopinathM. P. Attention aware fully convolutional deep learning model for retinal blood vessel segmentation.J. Intell. Fuzzy. Syst.202311110.3233/JIFS‑224229
     [Google Scholar]
/content/journals/eng/10.2174/0118722121248136230928053214
Loading
IoT based Predictive Modeling Techniques for Cancer Detection in Healthcare Systems
Recent Pat Eng 19, E241023222590 (2025); https://doi.org/10.2174/0118722121248136230928053214
/content/journals/eng/10.2174/0118722121248136230928053214
/content/journals/eng/10.2174/0118722121248136230928053214
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): cancer detection; convolutional neural networks; fuzzy C means; healthcare systems; IoT; machine learning

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