IoT enabled lung cancer detection and routing algorithm using CBSOA‐based ShCNN

Summary The Internet of Things (IoT) has tremendously spread worldwide, and it influenced the world through easy connectivity, interoperability, and interconnectivity using IoT devices. Numerous techniques have been developed using IoT‐enabled health care systems for cancer detection, but some limit...

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Published in:International journal of adaptive control and signal processing Vol. 37; no. 1; pp. 224 - 243
Main Authors: Gnanasigamani Samuel Raj, Emil Selvan, Diana Jeba Jingle, Issac, Maram, Balajee, Ananth, John Patrick
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 01.01.2023
Wiley Subscription Services, Inc
Subjects:
Algorithms
Artificial neural networks
cancer detection
Data acquisition
Internet of Things
Lung cancer
Optimization
Optimization algorithms
routing
Routing (telecommunications)
shepherd convolutional neural network
social optimization algorithm
ISSN:0890-6327, 1099-1115
Online Access:Get full text
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Summary:Summary The Internet of Things (IoT) has tremendously spread worldwide, and it influenced the world through easy connectivity, interoperability, and interconnectivity using IoT devices. Numerous techniques have been developed using IoT‐enabled health care systems for cancer detection, but some limitations exist in transmitting the health data to the cloud. The limitations can be accomplished using the proposed chronological‐based social optimization algorithm (CBSOA) that effectively transmits the patient's health data using IoT network, thereby detecting lung cancer in an effective way. Initially, nodes in the IoT network are simulated such that patient's health data are collected, and for transmission of such data, routing is performed in order to transmit the health data from source to destination through a gateway based on cloud service using CBSOA. The fitness is newly modeled by assuming the factors like energy, distance, trust, delay, and link quality. Finally, lung cancer detection is carried out at the destination point. At the destination point, the acquired input data is fed to preprocessing phase to make the data acceptable for further mechanism using data normalization. Once the feature selection is done using Canberra distance, then the lung cancer detection is performed using shepard convolutional neural network (ShCNN). The process of routing as well as training of ShCNN is performed using the CBSOA algorithm, which is devised by the inclusion of the chronological concept into the social optimization algorithm. The proposed approach has achieved a maximum accuracy of 0.940, maximum sensitivity of 0.941, maximum specificity of 0.928, and minimum energy of 0.452.
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ISSN:0890-6327
1099-1115
DOI:10.1002/acs.3518