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

I Jasmine Selvakumari Jeya; Dinesh Valluru; A Sherin

doi:10.17485/IJST/v15i42.1435

Article

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

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v15i42.1435

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

I Jasmine Selvakumari Jeya^1*, Dinesh Valluru², A Sherin³

¹Professor, Department of Information Technology, Hindusthan College of Engineering and Technology, Coimbatore, 641032, Tamil Nadu, India
²Associate Professor, Department of Computer Science and Engineering, SRK Institute of Technology, Vijayawada, 521 108, Andhra Pradesh, India
³Assistant Professor, Department of Computer Science and Engineering, Hindusthan College of Engineering and Technology, Coimbatore, 641 032, Tamil Nadu, India

*Corresponding Author
Email: [email protected]

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

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

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

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Copyright

© 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)