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A Novel IoT-Enabled Healthcare Monitoring Framework and Improved Grey Wolf Optimization Algorithm-Based Deep Convolution Neural Network Model for Early Diagnosis of Lung Cancer.

Authors :
Irshad, Reyazur Rashid
Hussain, Shahid
Sohail, Shahab Saquib
Zamani, Abu Sarwar
Madsen, Dag Øivind
Alattab, Ahmed Abdu
Ahmed, Abdallah Ahmed Alzupair
Norain, Khalid Ahmed Abdallah
Alsaiari, Omar Ali Saleh
Source :
Sensors (14248220). Mar2023, Vol. 23 Issue 6, p2932. 16p.
Publication Year :
2023

Abstract

Lung cancer is a high-risk disease that causes mortality worldwide; nevertheless, lung nodules are the main manifestation that can help to diagnose lung cancer at an early stage, lowering the workload of radiologists and boosting the rate of diagnosis. Artificial intelligence-based neural networks are promising technologies for automatically detecting lung nodules employing patient monitoring data acquired from sensor technology through an Internet-of-Things (IoT)-based patient monitoring system. However, the standard neural networks rely on manually acquired features, which reduces the effectiveness of detection. In this paper, we provide a novel IoT-enabled healthcare monitoring platform and an improved grey-wolf optimization (IGWO)-based deep convulution neural network (DCNN) model for lung cancer detection. The Tasmanian Devil Optimization (TDO) algorithm is utilized to select the most pertinent features for diagnosing lung nodules, and the convergence rate of the standard grey wolf optimization (GWO) algorithm is modified, resulting in an improved GWO algorithm. Consequently, an IGWO-based DCNN is trained on the optimal features obtained from the IoT platform, and the findings are saved in the cloud for the doctor's judgment. The model is built on an Android platform with DCNN-enabled Python libraries, and the findings are evaluated against cutting-edge lung cancer detection models. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14248220
Volume :
23
Issue :
6
Database :
Academic Search Index
Journal :
Sensors (14248220)
Publication Type :
Academic Journal
Accession number :
162813717
Full Text :
https://doi.org/10.3390/s23062932