Title: Improved cervix lesion classification using multi-objective binary firefly algorithm-based feature selection

Authors: Anita Sahoo; Satish Chandra

Addresses: Department of Computer Science and Engineering, JSS Academy of Technical Education, NOIDA, 201301, India ' Department of Computer Science, Jaypee Institute of Information Technology, NOIDA, 201301, India

Abstract: Cervical cancer is one of the vital and most frequent cancers, but can be cured if correctly diagnosed. This work is a novel effort towards developing a methodology for effective characterisation of cervix lesions that may assist radiologists in the diagnostic process by providing a reliable and objective discrimination of benign and malignant lesions in contrast enhanced CT-Scan images. Feature selection, which is a key stage in building such efficient classification models, is NP-hard; where, randomised algorithms do better. Since, firefly algorithm is an efficient biologically inspired randomised algorithm; here it has been utilised for optimal feature selection. This paper presents a multi-objective binary firefly algorithm for wrapper-based feature selection and utilises the selected feature subset for improved classification of cervix lesions. For experiments, contrast enhanced CT-Scan images of 22 patients have been used, where all lesions had been recommended for surgical biopsy by specialists. For characterisation of lesions, grey-level cooccurrence matrix-based texture features are extracted from two-level decomposition of wavelet coefficients. The objective function is designed to minimise the classification error and feature subset length both; making it multi-objective. With 94% accuracy in lesion classification, it has superior performance and greatly reduced execution time than multi-objective genetic algorithm-based feature selection.

Keywords: cervix lesions; cervix lesion characterisation; lesion classification; firefly algorithm; multi-objective optimisation; textural features; wavelet features; wrapper-based feature selection; bio-inspired computation; cervical cancer; benign lesions; malignant lesions; CT scans; computed tomography; medical images.

DOI: 10.1504/IJBIC.2016.081326

International Journal of Bio-Inspired Computation, 2016 Vol.8 No.6, pp.367 - 378

Available online: 28 Dec 2016 *

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