Abstract
Lung cancer is the prevalent and dangerous among different types of cancerous diseases. Lung cancer is one of the several leading causes of cancer-related deaths across the globe. A traditional way to detect lung cancer was carried out by trained professionals manually, by evaluating different parameters. However, this procedure was only helpful in the detection of cancer. Early analysis and diagnosis of such a sort of cancer growth is very important to save the life of a patient. Several healthcare systems assisted by artificial intelligence have emerged in the past to offer distinguishable services. This article presents a fog–cloud computing-assisted computer-aided system for the early diagnosis and management of lung cancer diseases. An image processing-based pre-processing step has been introduced to detect different abnormalities by feature extraction from CT scanned images at the fog layer. A backpropagation-based artificial neural network (ANN) has been utilized for the classification of different extracted features. Finally, the classified results are stored in a cloud layer for communication with doctors and caretakers. Moreover, several statistical measures such as: specificity, accuracy, and sensitivity have been calculated to assess the performance of the model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cruz CSD, Tanoue LT, Matthay RA (2011) Lung cancer: epidemiology, etiology, and prevention. Clin Chest Med 32(4):605–644
Didkowska J, Wojciechowska U, Mańczuk M, Łobaszewski J (2011) Lung cancer epidemiology: contemporary and future challenges worldwide. Ann Transl Med 4(8)
Alberg AJ, Samet JM (2003) Epidemiology of lung cancer. Chest 123(1):21S–49S
Selvanambi R, Natarajan J, Karuppiah M, Islam SH, Hassan MM, Fortino G (2020) Lung cancer prediction using higher-order recurrent neural network based on glowworm swarm optimization. Neural Comput Appl 32(9):4373–4386
Khalil AM, Li SG, Lin Y, Li HX, Ma SG (2020) A new expert system in the prediction of lung cancer disease based on fuzzy soft sets. Soft Comput 1–29
Dubey AK, Gupta U, Jain S (2016) Epidemiology of lung cancer and approaches for its prediction: a systematic review and analysis. Chin J Cancer 35(1):71
Agrawal A, Misra S, Narayanan R, Polepeddi L, Choudhary A (2011) A lung cancer outcome calculator using ensemble data mining on seer data. In: Proceedings of the tenth international workshop on data mining in bioinformatics, pp 1–9
Azzawi H, Hou J, Xiang Y, Alanni R (2016) Lung cancer prediction from microarray data by gene expression programming. IET Syst Biol 10(5):168–178
Nasser IM, Abu-Naser SS (2019) Lung cancer detection using artificial neural network. Int J Eng Inf Syst (IJEAIS) 3(3):17–23
Lakshmanaprabu S, Mohanty SN, Shankar K, Arunkumar N, Ramirez G (2019) Optimal deep learning model for classification of lung cancer on ct images. Future Gener Comput Syst 92:374–382
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M et al (2013) The cancer imaging archive (tcia): maintaining and operating a public information repository. J Digital Imaging 26(6):1045–1057
Hossin M, Sulaiman MN (2015) A review on evaluation metrics for data classification evaluations. Int J Data Min Knowl Manag Process 5(2):1
Acknowledgements
The work was completed under the project titled “Fog-Cloud Centric IoT assisted Technologies in Healthcare” with sanction number BGSBU/TEQIP-III/RGS/004 supported under the TEQIP-III Research Grant Scheme (RGS) of the National Project Implementation Unit (NPIU), a unit of the Ministry of Education (MoE), Government of India and the World Bank. The authors are grateful to the funding agency for the financial and infrastructural support provided to carry out the research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gupta, A., Jain, V., Hussain, W. (2021). A Fog–Cloud Computing-Inspired Image Processing-Based Framework for Lung Cancer Diagnosis Using Deep Learning. In: Sheth, A., Sinhal, A., Shrivastava, A., Pandey, A.K. (eds) Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-2248-9_3
Download citation
DOI: https://doi.org/10.1007/978-981-16-2248-9_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2247-2
Online ISBN: 978-981-16-2248-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)