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

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