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

© The Institution of Engineering and Technology
Received 11/02/2019, Accepted 24/03/2020, Revised 12/02/2020, Published 02/04/2020

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.

Inspec keywords: feature extraction; optimisation; lung; support vector machines; image segmentation; pattern clustering; feature selection; medical image processing; nearest neighbour methods; cancer; computerised tomography; image classification; graph theory; random forests

Other keywords: morphological operations; modified graph clustering-based whale optimisation algorithm; cancerous cells; cancer death; K-nearest neighbour; whale optimisation-based feature selection technique; lung regions; radiomic features; imaging technique; computed tomography; noise removal; normal cancer; histogram analysis; medical field; classification accuracy; F-measure; support vector machine; feature extraction stage; optimum feature selection; thresholding operations; CT lung image; advanced lung cancer classification approach; random forest; hybrid ensemble classifier

Subjects: Optimisation techniques; Computer vision and image processing techniques; Data handling techniques; Combinatorial mathematics; Biology and medical computing; X-rays and particle beams (medical uses); Patient diagnostic methods and instrumentation; X-ray techniques: radiography and computed tomography (biomedical imaging/measurement); Image recognition; Combinatorial mathematics; Knowledge engineering techniques; Algebra, set theory, and graph theory; Optimisation techniques

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