Abstract
Image processing is an innovative method to convert the real image into a sharp digital image by applying various functions upon it. However, it is a difficult task for physicians in the medical field. The significant difficulty is with the segmentation of images due to the blurred contrast and artifacts at the boundary edges. Hence in this paper, an efficient and adaptive fuzzy-GLCM based segmentation method was proposed. The images derive from the process of bronchoscopy. The ultimate goal of the proposed methodology was the accurate recognition of the lung carcinoma, which undergoes segmentation. The adaptive F-GLCM segmentation method enables the early and easy detection of lung cancer, which helps both the physicians and the patients for proper initial medication. Then the classification was done with the help of the GoogLeNet CNN architecture, which will reveal whether the cancerous growth was in a benign or in a malignant stage. Then the performance analysis of the proposed method was measured by comparing it with the other existing methodology.
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04 July 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04267-0
References
Bonta LR, Kiran NU (2019) Efficient segmentation of medical images using dilated residual networks. In computer aided intervention and diagnostics in clinical and medical images. Springer, Cham, pp 39–47
Chaudhary A, Singh SS (2012). Lung cancer detection on CT images by using image processing. International conference on computing sciences.
Chunhua Xu, Hao K, Song Y, Like Yu, Hou Z, Zhan P (2016) Early diagnosis of solitary pulmonary nodules. Nanjing Chest Hospital, Nanjing
Clarke LP, Croft BY, Staab E, Baker H, Sullivan DC (2001) Academic radiology. Natl Cancer Inst Initiat 8(5):447–450
Daoud A, Laktineh A, El-Zein S, Soubani AO (2019) Unusual presentation of primary lung adenocarcinoma mimicking pneumonia: case report and literature review. Respir Med Case Rep 28:100881. https://doi.org/10.1016/j.rmcr.2019.100881
Feng P-H, Chen T-T, Lin Y-T, Chiang S-Y, Lo C-M (2018) Classification of lung cancer subtypes based on autofluorescence bronchoscopic pattern recognition: a preliminary study. Comput Methods Programs Biomed 163:33–38. https://doi.org/10.1016/j.cmpb.2018.05.016
Ghosh P, Mitchell M (2006) Segmentation of medical images using a genetic algorithm. Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation-GECCO’06.
Han Y, Kim HJ, Kong KA, Kim SJ, Lee SH, Ryu YJ, Chang JH (2018) Diagnosis of small pulmonary lesions by transbronchial lung biopsy with radial endobronchial ultrasound and virtual bronchoscopic navigation versus CT-guided transthoracic needle biopsy: a systematic review and meta-analysis. PLoS ONE 13(1):e0191590. https://doi.org/10.1371/journal.pone.0191590
Han B, Han Y, Gao X, Zhang L (2019) Boundary constraint factor embedded localizing active contour model for medical image segmentation. J Ambient Intell Human Comput 10(10):3853–3862
Kalavathi P (2013) Brain tissue segmentation in MR brain images using multiple Otsu’s thresholding technique. 8th International conference on computer science and education.
Kanazawa K, Niki N, Satoh H, Komatsu H, Moriyama N (2018) Computer-assisted diagnosis of lung cancer using helical X-ray CT. Proceedings of the IEEE workshop on biomedical image analysis.
Kasales CJ, Hopper KD, Ariola DN, TenHave TR, Meilstrup JW, Mahraj RP, Barr JD (2000) Reconstructed helical CT scans: improvement in z-axis resolution compared with overlapped and non overlapped conventional CT scans. Am J Roentgenol 164(5):1281–1284
Lavanya M, Muthu Kannan P (2018) Lung lesion detection in ct scan images using the fuzzy local information cluster means (FLICM) automatic segmentation algorithm and back propagation network classification
Lynch DA, Sverzellati N, Travis WD, Brown KK, Colby TV, Galvin JR, Wells AU (2018) Diagnostic criteria for idiopathic pulmonary fibrosis: a Fleischner Society White Paper. Lancet Respir Med 6(2):138–153. https://doi.org/10.1016/s2213-2600(17)30433-2
Manickavasagam R, Selvan S (2019) Automatic detection and classification of lung nodules in CT image using optimized neuro-fuzzy classifier with cuckoo search algorithm. J Med Syst, 43(3).
Mehta AC, Hood KL, Schwarz Y, Solomon SB (2018) The evolutional history of electromagnetic navigation bronchoscopy. Chest. https://doi.org/10.1016/j.chest.2018.04.029
Murgu SD (2019) Robotic assisted-bronchoscopy: technical tips and lessons learned from the initial experience with a sampling of peripheral lung lesions. BMC Pulm Med. https://doi.org/10.1186/s12890-019-0857-z
Nagai K, Kuriyama K, Inoue A, Yoshida Y, Takami K (2017) Computed tomography-guided preoperative localization of small lung nodules with indocyanine green. Acta Radiol 59(7):830–835. https://doi.org/10.1177/0284185117733507
Okachi S, Imaizumi K, Imai N, Shimizu T, Hase T, Morise M, Hasegawa Y (2018) Safety and efficacy of flexible diagnostic bronchoscopy in ancient patients with lung cancer. Eur Geriatr Med 9(2):255–262. https://doi.org/10.1007/s41999-018-0033-7
Ozturk A, Demirci NY, Aktas Z, Demirag F, Alagoz A, Alici İO, Yilmaz A (2016) EBUS may arise as an initial time-saving procedure in patients who are suspected of having small cell lung cancer. Clin Respir J 12(2):517–523. https://doi.org/10.1111/crj.12556
Petrella F, Casiraghi M, Prisciandaro E, Gherzi L, Spaggiari L (2019) Incidental diagnosis of pulmonary mycobacteriosis among patients scheduled for lung cancer surgery: results from a series of 3224 consecutive operations. Heliyon 5(3):e01395
Raja NSM, Fernandes S, Dey N, Satapathy SC, Rajinikanth V (2018) Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation. J Ambient Intell Human Comput, pp 1–12
Ramírez, E., Sánchez, C., Borràs, A., Diez-Ferrer, M., Rosell, A., Gil, D. (2018) Image-based bronchial anatomy codification for biopsy guiding in video bronchoscopy. OR 2.0 context-aware operating theaters, computer assisted robotic endoscopy, clinical image-based procedures, and skin image analysis, pp 214–222. Doi: 10.1007/978-3-030-01201-4_23.
Ruiz MD, Grant J, Hernandez J (2018) Bronchoscopic debulking of a feline tracheobronchial carcinoma and long-term outcome. J Feline Med Surg Open Rep 4(1):205511691876770. https://doi.org/10.1177/2055116918767706
Ryan BM (2016) Differential eligibility of African American and European American lung cancer cases using LDCT screening guidelines. BMJ Open Respir Res 3(1):e000166
Sharif M, Tanvir U, Munir EU, Khan MA, Yasmin M (2018). Brain tumor segmentation and classification by improved binomial thresholding and multi-features selection. J Ambient Intell Human Comput, pp 1–20.
Shaukat F, Raja G, Ashraf R, Khalid S, Ahmad M, Ali A (2019) Artificial neural network based classification of lung nodules in CT images using intensity, shape and texture features. J Ambient Intell Human Comput 10(10):4135–4149
Short MA, Lam S, McWilliams AM, Ionescu DN, Zeng H (2011) Using laser Raman spectroscopy to reduce false positives of autofluorescence bronchoscopies: a pilot study. J Thorac Oncol 6(7):1206–1214
Skovgaard Christiansen I, Kuijvenhoven JC, Bodger U, Naur TMH, Ahmad K, Singh Sidhu J, Clements PF (2018) Endoscopic ultrasound with bronchoscope-guided fine needle aspiration for the diagnosis of paraesophageally located lung lesions. Respiration. https://doi.org/10.1159/000492578
Tan T, Li Z, Liu H, Zanjani FG, Ouyang Q, Tang Y, Li Q (2018) Optimize transfer learning for lung diseases in bronchoscopy using a new concept: sequential fine-tuning. IEEE J Transl Eng Health Med. https://doi.org/10.1109/jtehm.2018.2865787
Tripathi P, Tyagi S, Nath M (2019) A comparative analysis of segmentation techniques for lung cancer detection. Pattern Recognit Image Anal 29(1):167–173
Wani MA, Batchelor BG (1994) Edge-region-based segmentation of range images. IEEE Trans Pattern Anal Mach Intell 16(3):314–319
Zhu J, Tang F, Gu Y (2018) A prospective study on the diagnosis of peripheral lung cancer using endobronchial ultrasonography with a guide sheath and computed tomography-guided transthoracic needle aspiration. Ther Adv Med Oncol 10:175883401775226. https://doi.org/10.1177/1758834017752269
Zhu W, Xian L, Wang E, Hou Y (2019) Learning classification of big medical imaging data based on partial differential equation. J Ambient Intell Human Comput, pp 1–10.
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Yamunadevi, M.M., Ranjani, S.S. RETRACTED ARTICLE: Efficient segmentation of the lung carcinoma by adaptive fuzzy–GLCM (AF-GLCM) with deep learning based classification. J Ambient Intell Human Comput 12, 4715–4725 (2021). https://doi.org/10.1007/s12652-020-01874-7
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DOI: https://doi.org/10.1007/s12652-020-01874-7