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RETRACTED ARTICLE: Improved watershed histogram thresholding with probabilistic neural networks for lung cancer diagnosis for CBMIR systems

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This article was retracted on 15 June 2023

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Abstract

The past few decades have witnessed a steep increase in image data analysis for lung cancer, leading to huge repositories in the area of research in the medical sector. Content Based medical Image Retrieval (CBMIR) methods for lung cancer have been tried with the objective of facilitating access to image data. Many research works have been developed in content based medical image retrieval. But the techniques have the drawback of low efficiency and high computation cost. Image segmentation, extraction and classification methods of various kinds was taken upusing traditional methodswhich involves extraction of a specific region of interest and given to medical experts for diagnosis. The extracted region of interest region provides information useful for the for diagnosis of the disease. But the segmentation methods have some limitations such as flat valleys, noise sensitive and computational expensive which lead to reduction in the entire system performance. This is addressed by animproved watershed histogram thresholding using the probabilistic neural networks (IWHT-PNN) approach. The algorithm introduced outperforms the existing techniques improving the segmentation ratio and recognition accuracy of lung cancer which can be validated using experimental analysis.

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References

  1. Abdillah B, Bustamam A, Sarwinda D (2017) Image processing based detection of lung cancer on CT scan images. J Phys Conf Ser 893(1):012063 IOP Publishing

    Article  Google Scholar 

  2. AboulDahab D, Ghoniemy SSA, Selim GM (2012) Automated brain tumor detection and identification using image processing and probabilistic neural network techniques. International Journal of Image Processing and Visual Communication 1(2):1–8

    Google Scholar 

  3. Al-Tarawneh MS (2012) Lung cancer detection using image processing techniques. Leonardo Electronic Journal of Practices and Technologies 11(21):147–158

    Google Scholar 

  4. Ayşe MT, Güler İ (2014) Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks. IEEE J Biomed Health Inform 19(4):1451–1458

    Google Scholar 

  5. Caverly TJ, Cao P, Hayward RA, Meza R (2018) Identifying patients for whom lung cancer screening is preference-sensitive: a microsimulation study. Ann Intern Med 169:1–9

    Article  Google Scholar 

  6. Chaudhary A, Singh SS (2012) Lung cancer detection on CT images by using image processing. In: 2012 international conference on computing sciences. IEEE, pp 142–146

  7. El-Melegy MT, Mokhtar HM (2014) Tumor segmentation in brain MRI using a fuzzy approach with class center priors. EURASIP Journal on Image and Video Processing 21:2–14

    Google Scholar 

  8. El-Regaily SA, Salem MA, Abdel Aziz MH, Roushdy MI (2018) Survey of computer aided detection systems for lung cancer in computed tomography. Current Medical Imaging Reviews 14(1):3–18

    Article  Google Scholar 

  9. Gaikwad A, Inamdar A, Behera V (2016) Lung cancer detection using digital image processing on CT scan images. International Research Journal of Engineering and Technology (IRJET) in IEEE e-ISSN, 2395-0056

  10. Gomathi P, Baskar S, Shakeel PM et al (2019) Identifying brain abnormalities from electroencephalogram using evolutionary gravitational neocognitron neural network. Multimed Tools Appl:1–20. https://doi.org/10.1007/s11042-019-7301-5

  11. Huang WY, Daugherty SE, Shiels MS, Purdue MP, Freedman ND, Abnet CC, … Berndt SI (2018) Aspirin use and mortality in two contemporary US cohorts. Epidemiology 29(1):126–133

    Article  Google Scholar 

  12. Masri M, McManus M, Mudad R (2018) Treatment of advanced non-small cell lung cancer in the era of targeted therapy. Current Pulmonology Reports 7(3):79–91

    Article  Google Scholar 

  13. Mohamed Shakeel P, El. Tobely TE, Al-Feel H, Manogaran G, Baskar S (2019) Neural network based brain tumor detection using wireless infrared imaging sensor. IEEE Access:1

  14. Robichaux JP, Elamin YY, Tan Z, Carter BW, Zhang S, Liu S, … Le AT (2018) Mechanisms and clinical activity of an EGFR and HER2 exon 20–selective kinase inhibitor in non–small cell lung cancer. Nat Med 24(5):638

    Article  Google Scholar 

  15. Schmid U, Liesenfeld KH, Fleury A, Dallinger C, Freiwald M (2018) Population pharmacokinetics of nintedanib, an inhibitor of tyrosine kinases, in patients with non-small cell lung cancer or idiopathic pulmonary fibrosis. Cancer Chemother Pharmacol 81(1):89–101

    Article  Google Scholar 

  16. Sene A, Kamsu-Foguem B, Rumeau P (2018) Decision support system for in-flight emergency events. Cogn Tech Work 20(2):245–266

    Article  Google Scholar 

  17. Shakeel PM, Manogaran G (2018) Prostate cancer classification from prostate biomedical data using ant rough set algorithm with radial trained extreme learning neural network. Heal Technol:1–9. https://doi.org/10.1007/s12553-018-0279-6

  18. Shakeel PM, Baskar S, Dhulipala VS, Mishra S, Jaber MM (2018) Maintaining security and privacy in health care system using learning based deep-Q-networks. J Med Syst 42(10):186. https://doi.org/10.1007/s10916-018-1045-z

    Article  Google Scholar 

  19. Smith A, Mullooly M, Murphy L, Barron TI, Bennett K (2018) Associations between obesity, smoking and lymph node status at breast cancer diagnosis in the prostate, lung, colorectal and ovarian (PLCO) cancer screening trial. PLoS One 13(8):e0202291

    Article  Google Scholar 

  20. Sridhar KP, Baskar S, Shakeel PM, Dhulipala VS (2018) Developing brain abnormality recognize system using multi-objective pattern producing neural network. J Ambient Intell Humaniz Comput:1–9. https://doi.org/10.1007/s12652-018-1058-y

  21. Tanner NT, Banas E, Yeager D, Dai L, Halbert CH, Silvestri GA (2019) In-person and telephonic shared decision-making visits for people considering lung cancer screening: an assessment of decision quality. Chest 155(1):236–238

    Article  Google Scholar 

  22. Tiwari S (2018) An analysis in tissue classification for colorectal cancer histology using convolution neural network and colour models. International Journal of Information System Modeling and Design (IJISMD) in ACM 9(4):1–19

    Google Scholar 

  23. Wang X, Mao K, Wang L, Yang P, Lu D, He P (2019) An appraisal of lung nodules automatic classification algorithms for CT images. Sensors 19(1):194

    Article  Google Scholar 

  24. Wei G, Cao H, Ma H, Qi S, Qian W, Ma Z (2018) Content-based image retrieval for lung nodule classification using texture features and learned distance metric. J Med Syst 42(1):13

    Article  Google Scholar 

  25. William W, Basaza-Ejiri AH, Obungoloch J, Ware A (2018) A review of applications of image analysis and machine learning techniques in automated diagnosis and classification of cervical cancer from pap-smear images. In: 2018 IST-Africa week conference (IST-Africa). IEEE, p 1

  26. Xu J, Zhao X, He D, Wang J, Li W, Liu Y, … Gu M (2018) Loss of EGFR confers acquired resistance to AZD9291 in an EGFR-mutant non-small cell lung cancer cell line with an epithelial–mesenchymal transition phenotype. J Cancer Res Clin Oncol:1–10

  27. Yang T, Cheng J, Zhu C (2018) A segmentation of pulmonary nodules based on improved fuzzy C-means clustering algorithm. In: MATEC web of conferences, vol 232. EDP Sciences, 03011

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Acknowledgments

The authors would like to thank the Fakulti Teknologi Maklumat dan Komunikasi (FTMK), Universiti Teknikal Malaysia Melaka (UTeM) for providing the facilities to conduct this study. Furthermore, thanks to Centre of Research and Innovation Management (CRIM), UTeM for financial assistance.

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Authors and Affiliations

  1. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia

    P. Mohamed Shakeel, Mohamad Ishak Desa & M. A. Burhanuddin

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  1. P. Mohamed Shakeel
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  2. Mohamad Ishak Desa
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  3. M. A. Burhanuddin
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Correspondence to P. Mohamed Shakeel.

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This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s11042-023-16006-4

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Mohamed Shakeel, P., Desa, M.I. & Burhanuddin, M.A. RETRACTED ARTICLE: Improved watershed histogram thresholding with probabilistic neural networks for lung cancer diagnosis for CBMIR systems. Multimed Tools Appl 79, 17115–17133 (2020). https://doi.org/10.1007/s11042-019-7662-9

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  • DOI: https://doi.org/10.1007/s11042-019-7662-9

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