Efficient segmentation of the lung carcinoma by adaptive fuzzy–GLCM (AF-GLCM) with deep learning based classification

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.

[1]  N. Sri Madhava Raja,et al.  Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation , 2018, J. Ambient Intell. Humaniz. Comput..

[2]  B. Ryan.  Differential eligibility of African American and European American lung cancer cases using LDCT screening guidelines , 2016, BMJ Open Respiratory Research.

[3]  S. S. Singh,et al.  Lung Cancer Detection on CT Images by Using Image Processing , 2012, 2012 International Conference on Computing Sciences.

[4]  İ. Alıcı,et al.  EBUS may arise as an initial time saving procedure in patients who are suspected to have small cell lung cancer , 2018, The clinical respiratory journal.

[5]  Debora Gil,et al.  Image-Based Bronchial Anatomy Codification for Biopsy Guiding in Video Bronchoscopy , 2018, OR 2.0/CARE/CLIP/ISIC@MICCAI.

[6]  Ehsan Ullah Munir,et al.  Brain tumor segmentation and classification by improved binomial thresholding and multi-features selection , 2018, J. Ambient Intell. Humaniz. Comput..

[7]  F. Petrella,et al.  Incidental diagnosis of pulmonary mycobacteriosis among patients scheduled for lung cancer surgery: results from a series of 3224 consecutive operations , 2019, Heliyon.

[8]  P Muthu Kannan,et al.  Lung Lesion Detection in CT Scan Images Using the Fuzzy Local Information Cluster Means (FLICM) Automatic Segmentation Algorithm and Back Propagation Network Classification , 2017, Asian Pacific journal of cancer prevention : APJCP.

[9]  Xinbo Gao,et al.  Boundary constraint factor embedded localizing active contour model for medical image segmentation , 2018, J. Ambient Intell. Humaniz. Comput..

[10]  Juan Hernandez,et al.  Bronchoscopic debulking of a feline tracheobronchial carcinoma and long-term outcome , 2018, JFMS open reports.

[11]  Asil Daoud,et al.  Unusual presentation of primary lung adenocarcinoma mimicking pneumonia: Case report and literature review , 2019, Respiratory medicine case reports.

[12]  D. Lynch,et al.  Diagnostic criteria for idiopathic pulmonary fibrosis: a Fleischner Society White Paper. , 2017, The Lancet. Respiratory medicine.

[13]  Bruce G. Batchelor,et al.  Edge-Region-Based Segmentation of Range Images , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  S. Selvan,et al.  Automatic Detection and Classification of Lung Nodules in CT Image Using Optimized Neuro Fuzzy Classifier with Cuckoo Search Algorithm , 2019, Journal of Medical Systems.

[15]  K D Hopper,et al.  Reconstructed helical CT scans: improvement in z-axis resolution compared with overlapped and nonoverlapped conventional CT scans. , 1995, AJR. American journal of roentgenology.

[16]  Haishan Zeng,et al.  Using Laser Raman Spectroscopy to Reduce False Positives of Autofluorescence Bronchoscopies: A Pilot Study , 2011, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[17]  Yani Hou,et al.  Learning classification of big medical imaging data based on partial differential equation , 2019, Journal of Ambient Intelligence and Humanized Computing.

[18]  Efficient Segmentation of Medical Images Using Dilated Residual Networks , 2019 .

[19]  Noboru Niki,et al.  Computer assisted diagnosis of lung cancer using helical X-ray CT , 1994, Proceedings of IEEE Workshop on Biomedical Image Analysis.

[20]  A. Inoue,et al.  Computed tomography-guided preoperative localization of small lung nodules with indocyanine green , 2018, Acta radiologica.

[21]  Y. Hasegawa,et al.  Safety and efficacy of diagnostic flexible bronchoscopy in very old patients with lung cancer , 2018, European Geriatric Medicine.

[22]  P. Kalavathi,et al.  Brain tissue segmentation in MR brain images using multiple Otsu's thresholding technique , 2013, 2013 8th International Conference on Computer Science & Education.

[23]  P. Clementsen,et al.  Endoscopic Ultrasound with Bronchoscope-Guided Fine Needle Aspiration for the Diagnosis of Paraesophageally Located Lung Lesions , 2018, Respiration.

[24]  Madhwendra Nath,et al.  A Comparative Analysis of Segmentation Techniques for Lung Cancer Detection , 2019, Pattern Recognition and Image Analysis.

[25]  L. Clarke,et al.  National Cancer Institute initiative: Lung image database resource for imaging research. , 2001, Academic radiology.

[26]  S. Solomon,et al.  The Evolutional History of Electromagnetic Navigation Bronchoscopy: State of the Art. , 2018, Chest.

[27]  Su Hwan Lee,et al.  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 , 2018, PloS one.

[28]  Payel Ghosh,et al.  Segmentation of medical images using a genetic algorithm , 2006, GECCO.

[29]  Shehzad Khalid,et al.  Artificial neural network based classification of lung nodules in CT images using intensity, shape and texture features , 2019, Journal of Ambient Intelligence and Humanized Computing.

[30]  S. Murgu.  Robotic assisted-bronchoscopy: technical tips and lessons learned from the initial experience with sampling peripheral lung lesions , 2019, BMC Pulmonary Medicine.

[31]  Chung-Ming Lo,et al.  Classification of lung cancer subtypes based on autofluorescence bronchoscopic pattern recognition: A preliminary study , 2018, Comput. Methods Programs Biomed..

[32]  Jun Zhu,et al.  A prospective study on the diagnosis of peripheral lung cancer using endobronchial ultrasonography with a guide sheath and computed tomography-guided transthoracic needle aspiration , 2018, Therapeutic advances in medical oncology.

[33]  Tao Tan,et al.  Optimize Transfer Learning for Lung Diseases in Bronchoscopy Using a New Concept: Sequential Fine-Tuning , 2018, IEEE Journal of Translational Engineering in Health and Medicine.