NDLI: Advanced lung cancer classification approach adopting modified graph clustering and whale optimisation-based feature selection technique accompanied by a hybrid ensemble classifier

Content Provider	IET Digital Library
Author	Priya, Michael Mary Adline Jawhar, S. Joseph
Abstract	Nowadays, lung cancer is the leading cause of cancer death in both men and women. The early detection of potentially cancerous cells is the best way to improve the patient's chances of survival. In the medical field, computed tomography (CT) is the best imaging technique and it is helpful for doctors to accurately find the cancerous cells. The authors propose an automatic approach to analyse and segment the lungs and classify each lung into normal or cancer. Initially, the CT lung image is pre-processed to remove noise. Then, they combine the histogram analysis with thresholding and morphological operations to segment and extract the lung regions. In feature extraction stage, the radiomic features of each lung image are extracted separately. Then to improve the classification accuracy, some of the optimum features are selected using modified graph clustering-based whale optimisation algorithm. Finally, the selected features are classified using ensemble classifiers such as support vector machine, K-nearest neighbour, and random forest. Experimental result demonstrates that the proposed method achieves better performance in terms of sensitivity, specificity, precision, recall, F-measure, and accuracy when compared with other state-of-art approaches.
Starting Page	2204
Ending Page	2215
Page Count	12
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 10, Aug (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0178
Journal	IET Image Processing
Publisher Date	2020-04-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Advanced Lung Cancer Classification Approach Algebra Biology And Medical Computing Biomedical Imaging/measurement Cancer Cancer Death Cancerous Cell Classification Accuracy Combinatorial Mathematics Computed Tomography Computer Vision And Image Processing Technique Computerised Tomography CT Lung Image Data Handling Technique F-measure Feature Extraction Feature Extraction Stage Feature Selection Graph Theory Histogram Analysis Hybrid Ensemble Classifier Image Classification Image Recognition Image Segmentation Imaging Technique K-nearest Neighbour Knowledge Engineering Technique Lung Lung Region Medical Field Medical Image Processing Modified Graph Clustering-based Whale Optimisation Algorithm Morphological Operation Nearest Neighbour Method Noise Removal Normal Cancer Optimisation Optimisation Technique Optimum Feature Selection Patient Diagnostic Method And Instrumentation Pattern Clustering Radiography And Computed Tomography Radiomic Feature Random Forest Set Theory Support Vector Machine Thresholding Operation Whale Optimisation-based Feature Selection Technique X-Ray Technique X-Rays And Particle Beam
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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