• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2022, Volume: 15, Issue: 42, Pages: 2219-2229

Original Article

Deep Learning based Mobilenet with Deep Belief Network for Lung Cancer Diagnosis in IOT and Cloud Enabled Environment

Received Date:10 July 2022, Accepted Date:01 September 2022, Published Date:10 November 2022


Background: The purpose of the present investigation is to unravel the complexity involved in Lung Cancer diagnosis by inculcating a new automated deep learning based MobileNet (DLMN) with deep belief network (DBN), called DLMN-DBN model in Internet of Things and cloud enabled environment. Methods: This paper presents an optimal DLMN-DBN model for lung cancer diagnosis where the parameters of DLMN-DBN model are optimized, and feature extraction and classification takes place by DLMN and DBN model respectively. The experimentation part takes place on four dimensions: 1) IoT devices enabled data acquisition for lung cancer diagnosis and the data are transmitted to the cloud server for diagnostic process, 2) the Guassian Filtering (GF) based preprocessing technique for noise removal, 3) feature extraction using DLMN model and 4) Optimal classification using DBN model. For experimental validation, an extensive experimentation analysis is performed to highlight the superior diagnostic outcome of the DLMN-DBN model. Findings: The experimental values stated that the DLMN-DBN model has resulted in superior results when compared with existing models with higher accuracy, sensitivity, and specificity of 95.55%, 93.94%, and 96.49% respectively. Novelty and applications: The new state of the art DLMN-DBN model and its robustness help general practitioners efficiently and effectively diagnose lung cancer conditions at the initial stage thereby reducing further complications and morbidity. Keywords: Lung cancer; Diagnosis; Classification; Deep learning; MobileNet; Deep belief network


  1. Mishra S, Thakkar HK, Mallick PK, Tiwari P, Alamri A. A sustainable IoHT based computationally intelligent healthcare monitoring system for lung cancer risk detection. Sustainable Cities and Society. 2021;72:103079. Available from: https://doi.org/10.1016/j.scs.2021.103079
  2. Valluru D, Jeya IJS. IoT with cloud based lung cancer diagnosis model using optimal support vector machine. Health Care Management Science. 2020;23(4):670–679. Available from: https://doi.org/10.1007/s10729-019-09489-x
  3. Revathi M, Jeya IJS, Deepa SN. Deep learning-based soft computing model for image classification application. Soft Computing. 2020;24(24):18411–18430. Available from: https://doi.org/10.1007/s00500-020-05048-7
  4. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians. 2021;71(3):209–249. Available from: https://doi.org/10.3322/caac.21660
  5. Mathur P, Sathishkumar K, Chaturvedi M, Das P, Sudarshan KL, Santhappan S, et al. Cancer Statistics, 2020: Report From National Cancer Registry Programme, India. JCO Global Oncology. 2020;6(6):1063–1075. Available from: https://doi.org/10.1200/GO.20.00122
  6. Shakeel PM, Tolba A, Al-Makhadmeh Z, Jaber MM. Automatic detection of lung cancer from biomedical data set using discrete AdaBoost optimized ensemble learning generalized neural networks. Neural Computing and Applications. 2020;32(3):777–790. Available from: http://doi.org/10.1007/s00521-018-03972-2
  7. Toraman S, Girgin M, Üstündağ B, Türkoğlu İ. Classification of the likelihood of colon cancer with machine learning techniquesusing FTIR signals obtained from plasma. Turk. J. Electr. Eng. Comput. Sci.. 2019;27(3):1765–1779. Available from: http://doi.org/10.3906/elk-1801-259
  8. Krishna SL, Jeya IJS, Deepa SN. Fuzzy-twin proximal SVM kernel-based deep learning neural network model for hyperspectral image classification. Neural Computing and Applications. 2022;34(21):19343–19376. Available from: https://doi.org/10.1007/s00521-022-07517-6
  9. Masud M, Muhammad G, Hossain MS, Alhumyani H, Alshamrani SS, Cheikhrouhou O, et al. Light Deep Model for Pulmonary Nodule Detection from CT Scan Images for Mobile Devices. Wireless Communications and Mobile Computing. 2020;2020:1–8. Available from: http://doi.org/10.1155/2020/8893494
  10. Shakeel PM, Burhanuddin MA, Desa MI. Automatic lung cancer detection from CT image using improved deep neural network and ensemble classifier. Neural Computing and Applications. 2022;34(12):9579–9592. Available from: http://doi.org/10.1007/s00521-020-04842-6
  11. Kumar R, Singh DK, Mishra AK. An Approach to Extract Fine Detail and Unclear Information by Enhancing Computed Tomography Image. 2nd International Conference on Data, Engineering and Applications (IDEA). 2020. Available from: https://doi.org/10.1109/IDEA49133.2020.9170735
  12. Masud M, Sikder N, Nahid AA, Bairagi AK, Alzain MA. A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework. Sensors. 2021;21(3):748. Available from: https://doi.org/10.3390/s21030748
  13. Habib S, Alsanea M, Aloraini M, Al-Rawashdeh HS, Islam M, Khan S. An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection. Sensors. 2602;22(7):2602. Available from: https://doi.org/10.3390/s22072602
  14. Nachimuthu DS, Jeya IJS, Baladhandapani AD. Adaptive extreme learning machine - Fuzzy system framework for energy optimization of IOTs in wireless sensor networks. Internet Technology Letters. 2021;(e267). Available from: https://doi.org/10.1002/itl2.267


© 2022 Jeya et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


Subscribe now for latest articles and news.