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. 2021 Nov 26;11(12):2208.
doi: 10.3390/diagnostics11122208.

VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images

Muhammad Attique Khan  1 , Venkatesan Rajinikanth  2 , Suresh Chandra Satapathy  3 , David Taniar  4 , Jnyana Ranjan Mohanty  5 , Usman Tariq  6 , Robertas Damaševičius  7
Affiliations

VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images

Muhammad Attique Khan et al. Diagnostics (Basel). .

Abstract

Pulmonary nodule is one of the lung diseases and its early diagnosis and treatment are essential to cure the patient. This paper introduces a deep learning framework to support the automated detection of lung nodules in computed tomography (CT) images. The proposed framework employs VGG-SegNet supported nodule mining and pre-trained DL-based classification to support automated lung nodule detection. The classification of lung CT images is implemented using the attained deep features, and then these features are serially concatenated with the handcrafted features, such as the Grey Level Co-Occurrence Matrix (GLCM), Local-Binary-Pattern (LBP) and Pyramid Histogram of Oriented Gradients (PHOG) to enhance the disease detection accuracy. The images used for experiments are collected from the LIDC-IDRI and Lung-PET-CT-Dx datasets. The experimental results attained show that the VGG19 architecture with concatenated deep and handcrafted features can achieve an accuracy of 97.83% with the SVM-RBF classifier.

Keywords: VGG-SegNet; deep learning; lung CT images; nodule detection; pre-trained VGG19.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Structure of the proposed lung-nodule segmentation and classification system.
Figure 2
Figure 2
Sample test images considered in this study.
Figure 3
Figure 3
Structure of VGG19 supported segmentation (VGG-SegNet) and classification scheme.
Figure 4
Figure 4
Results obtained with proposed VGG-SegNet scheme: (a) text image, (b) lung section enhanced by encoder, (c) localization of nodule by decoder and (d) extracted nodule by SoftMax unit.
Figure 5
Figure 5
Segmentation results attained with considered CNN models.
Figure 6
Figure 6
Segmentation of nodule from chosen images of Lung-PET-CT-Dx and LIDC-IDRI dataset.
Figure 7
Figure 7
LBP patterns generated from the sample image with various LBP weights.
Figure 8
Figure 8
PHOG features obtained with the sample test images of Normal/Nodule class.
Figure 9
Figure 9
Spider plot to compare the CT image classification performance of CNN models.
Figure 10
Figure 10
Training performance of the VGG19 with SVM-RBF for lung CT image slices.
Figure 11
Figure 11
Overall performance of VGG19 with various classifiers summarized as glyph-plots.
Figure 12
Figure 12
ROC curve attained for VGG19 with DF + HCF.
Figure 13
Figure 13
Validation of the disease detection accuracy of the proposed system with existing approaches.
See this image and copyright information in PMC

References

    1. WHO [(accessed on 21 September 2021)]. Available online: https://www.who.int/news-room/fact-sheets/detail/cancer.
    1. Olson E.J. [(accessed on 21 September 2021)]. Available online: https://www.mayoclinic.org/diseases-conditions/lung-cancer/expert-answer....
    1. Girvin F., Ko J.P. Pulmonary nodules: Detection, assessment, and CAD. Am. J. Roentgenol. 2008;191:1057–1069. doi: 10.2214/AJR.07.3472. - DOI - PubMed
    1. Bhandary A., Prabhu G.A., Rajinikanth V., Thanaraj K.P., Satapathy S.C., Robbins D.E., Shasky C., Zhang Y.D., Tavares J.M.R.S., Raja N.S.M. Deep-learning framework to detect lung abnormality—A study with chest X-Ray and lung CT scan images. Pattern Recognit. Lett. 2020;129:271–278. doi: 10.1016/j.patrec.2019.11.013. - DOI
    1. Choi W.J., Choi T.S. Automated pulmonary nodule detection system in computed tomography images: A hierarchical block classification approach. Entropy. 2013;15:507–523. doi: 10.3390/e15020507. - DOI

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