Skip to main content
SpringerLink
Log in

An Optimized Neural Network Model to Classify Lung Nodules from CT-Scan Images

  • Conference paper
  • First Online:
Communication and Intelligent Systems (ICCIS 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 968))

Included in the following conference series:

  • 17 Accesses

Abstract

The early identification of lung nodules in chest X-rays is vital for human life and can prevent health emergencies. Manual prediction of lung nodules is consistent, and at early stages of lung cancer, they cannot be predicted, so an artificial intelligence system is required to identify lung nodules at the early stage. So many researchers have worked on lung nodule prediction and classification by machine learning and deep learning, but the models implemented could be more robust and consistent. So, we have proposed a novel approach to detect lung nodules early using customized CNN. It can easily segment the small nodules in classification. And we used a kernel regularizer to avoid overfitting. This model was implemented on the LIDC-IDRI dataset from Kaggle with 25,000 samples. Finally, we got an accuracy of 0.951, with calculated precision, recall, and F1-score. With this, we can confirm that our model is consistently performing.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Liu W, Liu X, Luo X, Wang M, Han G, Zhao X, Zhu Z (2023) A pyramid input augmented multi-scale CNN for GGO detection in 3D lung CT images. Pattern Recogn 136:109261

    Article  Google Scholar 

  2. Tenescu A, Bercean BA, Avramescu C, Marcu M (2023) Averaging model weights boosts automated lung nodule detection on computed tomography. In: Proceedings of the 2023 13th international conference on bioscience, biochemistry and bioinformatics, pp 59–62

    Google Scholar 

  3. Bermejo-Peláez D, Ash SY, Washko GR, Estépar RSJ, Ledesma-Carbayo MJ (2020) Classification of interstitial lung abnormality patterns with an ensemble of deep convolutional neural networks. Sci Rep 10(1):338

    Article  Google Scholar 

  4. Zhou J, Hu B, Feng W, Zhang Z, Fu X, Shao H, Wang H, Jin L, Ai S, Ji Y (2023) An ensemble deep learning model for risk stratification of invasive lung adenocarcinoma using thin-slice CT. NPJ Digital Med 6(1):119

    Google Scholar 

  5. Liang H, Hu M, Ma Y, Yang L, Chen J, Lou L, Chen C, Xiao Y (2023) Performance of deep-learning solutions on lung nodule malignancy classification: a systematic review. Life 13(9):1911

    Google Scholar 

  6. Kadry S, Herrera-Viedma E, Crespo RG, Krishnamoorthy S, Rajinikanth V (2023) Automatic detection of lung nodules in CT scan slices using CNN segmentation schemes: a study. Procedia Comput Sci 218:2786–2794

    Google Scholar 

  7. Gugulothu VK, Balaji S (2023) A novel deep learning approach for the detection and classification of lung nodules from CT images. Multimedia Tools Appl 1–24

    Google Scholar 

  8. Annavarapu CSR, Parisapogu SAB, Keetha NV, Donta PK, Rajita G (2023) A Bi-FPN-based encoder–decoder model for lung nodule image segmentation. Diagnostics 13(8):1406

    Google Scholar 

  9. Naseer I, Akram S, Masood T, Rashid M, Jaffar A (2023) Lung cancer classification using modified u-net based lobe segmentation and nodule detection. IEEE Access

    Google Scholar 

  10. Halder A, Dey D (2023) Atrous convolution aided an integrated framework for lung nodule segmentation and classification. Biomed Signal Process Control 82:104527

    Article  Google Scholar 

  11. Keshani M, Azimifar Z, Tajeripour F, Boostani R (2013) Lung nodule segmentation and recognition using SVM classifier and active contour modeling: a complete intelligent system. Comput Biol Med 43(4):287–300

    Article  Google Scholar 

  12. El-Askary NS, Salem MA, Roushdy MI (2022) Features processing for random forest optimization in lung nodule localization. Expert Syst Appl 193:116489

    Google Scholar 

  13. Cao H, Liu H, Song E, Ma G, Xu X, Jin R, Liu T, Hung CC (2020) A two-stage convolutional neural network for lung nodule detection. IEEE J Biomed Health Inf 24(7):2006–2015

    Google Scholar 

  14. Gu Y, Lu X, Yang L, Zhang B, Yu D, Zhao Y, Gao L, Wu L, Zhou T (2018) Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs. Comput Biol Med 103:220–231

    Google Scholar 

  15. Zhao C, Han J, Jia Y, Gou F (2018) Lung nodule detection via 3D U-Net and contextual convolutional neural network. In: 2018 International conference on networking and network applications (NaNA). IEEE, pp 356–361

    Google Scholar 

  16. Xie H, Yang D, Sun N, Chen Z, Zhang Y (2019) Automated pulmonary nodule detection in CT images using deep convolutional neural networks. Pattern Recogn 85:109–119

    Article  Google Scholar 

  17. Zheng S, Guo J, Cui X, Veldhuis RNJ, Oudkerk M, Van Ooijen PMA (20119) Automatic pulmonary nodule detection in CT scans using convolutional neural networks based on maximum intensity projection. IEEE Trans Med Imaging 39(3):797–805

    Google Scholar 

  18. Tang H, Kim DR, Xie X (2018) Automated pulmonary nodule detection using 3D deep convolutional neural networks. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). IEEE pp 523–526

    Google Scholar 

  19. Zhang J, Xia Y, Cui H, Zhang Y (2018) Pulmonary nodule detection in medical images: a survey. Biomed Signal Process Control 43:138–147

    Article  Google Scholar 

  20. Schultheiss M, Schober SA, Lodde M, Bodden J, Aichele J, Mueller-Leisse C, Renger B, Pfeiffer F, Pfeiffer D (2020) A robust convolutional neural network for lung nodule detection in the presence of foreign bodies. Sci Rep 10(1):12987

    Article  Google Scholar 

  21. Jin H, Li Z, Tong R, Lin L (2018) A deep 3D residual CNN for false-positive reduction in pulmonary nodule detection. Med Phys 45(5):2097–2107

    Article  Google Scholar 

  22. Manickavasagam R, Selvan S, Selvan M (2022) CAD system for lung nodule detection using deep learning with CNN. Med Biol Eng Comput 60(1):221–228

    Google Scholar 

  23. Asiya, Sugitha, N, Automatically segmenting and classifying the lung nodules from CT images 2147–6799

    Google Scholar 

  24. https://www.kaggle.com/datasets/zhangweiled/lidcidri

Download references

Author information

Authors and Affiliations

  1. CSE Department, Noorul Islam Center for Higher Education, Kumarakovil, Thukalay, Tamil Nadu, India

    Asiya

  2. Saveetha Engineering College, Thandalam, Chennai, India

    N. Sugitha

Authors
  1. Asiya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. Sugitha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Asiya .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

  2. Department of Electrical and Electronics Engineering, National Institute of Technology Uttarakhand, Srinagar, Uttarakhand, India

    Vivek Shrivastava

  3. Department of Computer Science, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Ashish Kumar Tripathi

  4. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Asiya, Sugitha, N. (2024). An Optimized Neural Network Model to Classify Lung Nodules from CT-Scan Images. In: Sharma, H., Shrivastava, V., Tripathi, A.K., Wang, L. (eds) Communication and Intelligent Systems. ICCIS 2023. Lecture Notes in Networks and Systems, vol 968. Springer, Singapore. https://doi.org/10.1007/978-981-97-2079-8_32

Download citation

Publish with us

Policies and ethics

Search

Navigation