IoT based lung cancer detection using machine learning and cuckoo search optimization
International Journal of Pervasive Computing and Communications
ISSN: 1742-7371
Article publication date: 17 June 2021
Issue publication date: 10 December 2021
Abstract
Purpose
Detecting cancer from the computed tomography (CT)images of lung nodules is very challenging for radiologists. Early detection of cancer helps to provide better treatment in advance and to enhance the recovery rate. Although a lot of research is being carried out to process clinical images, it still requires improvement to attain high reliability and accuracy. The main purpose of this paper is to achieve high accuracy in detecting and classifying the lung cancer and assisting the radiologists to detect cancer by using CT images. The CT images are collected from health-care centres and remote places through Internet of Things (IoT)-enabled platform and the image processing is carried out in the cloud servers.
Design/methodology/approach
IoT-based lung cancer detection is proposed to access the lung CT images from any remote place and to provide high accuracy in image processing. Here, the exact separation of lung nodule is performed by Otsu thresholding segmentation with the help of optimal characteristics and cuckoo search algorithm. The important features of the lung nodules are extracted by local binary pattern. From the extracted features, support vector machine (SVM) classifier is trained to recognize whether the lung nodule is malicious or non-malicious.
Findings
The proposed framework achieves 99.59% in accuracy, 99.31% in sensitivity and 71% in peak signal to noise ratio. The outcomes show that the proposed method has achieved high accuracy than other conventional methods in early detection of lung cancer.
Practical implications
The proposed algorithm is implemented and tested by using more than 500 images which are collected from public and private databases. The proposed research framework can be used to implement contextual diagnostic analysis.
Originality/value
The cancer nodules in CT images are precisely segmented by integrating the algorithms of cuckoo search and Otsu thresholding in order to classify malicious and non-malicious nodules.
Keywords
Acknowledgements
The authors express sincere thanks to Dr M. Vijay Kumar, Radiologist from Star Diagnostics Hospital at Ananthapuramu, for providing the lung CT images to carry-out this research work. Also, the first author expresses heartful thanks to management of K L Deemed to be University, Vaddeswaram, Guntur, Andhra Pradesh, where he is a research scholar and Annamacharya Institute of Technology and Sciences, Rajampet, A.P. for providing good research facilities.
Citation
Chapala, V. and Bojja, P. (2021), "IoT based lung cancer detection using machine learning and cuckoo search optimization", International Journal of Pervasive Computing and Communications, Vol. 17 No. 5, pp. 549-562. https://doi.org/10.1108/IJPCC-10-2020-0160
Publisher
:Emerald Publishing Limited
Copyright © 2021, Emerald Publishing Limited