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

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