Preprint Article Version 1 This version not peer reviewed

A Quantum Hybrid PSO Combined with Fuzzy K-NN Approach to Feature Selection and Cell Classification in Cervical Cancer Detection

Version 1 : Received: 30 November 2017 / Approved: 30 November 2017 / Online: 30 November 2017 (07:21:00 CET)

How to cite: Iliyasu, A.M.; Fatichah, C. A Quantum Hybrid PSO Combined with Fuzzy K-NN Approach to Feature Selection and Cell Classification in Cervical Cancer Detection. Preprints 2017, 2017110193 (doi: 10.20944/preprints201711.0193.v1). Iliyasu, A.M.; Fatichah, C. A Quantum Hybrid PSO Combined with Fuzzy K-NN Approach to Feature Selection and Cell Classification in Cervical Cancer Detection. Preprints 2017, 2017110193 (doi: 10.20944/preprints201711.0193.v1).

Abstract

A quantum hybrid (QH) intelligent approach that blends the adaptive search capability of the quantum-behaved particle swarm optimisation (QPSO) method with the intuitionistic rationality of traditional fuzzy k-nearest neighbours (Fuzzy k-NN) algorithm (known simply as the Q-Fuzzy approach) is proposed for efficient feature selection and classification of cells in cervical smeared (CS) images. From an initial multitude of seventeen (17) features describing the geometry, colour, and texture of the CS images, the QPSO stage of our proposed technique is used to select the best subset features (i.e. global best particles) that represent a pruned down collection of seven (7) features. Using a dataset of almost 1000 images, performance evaluation of our proposed Q-Fuzzy approach assesses the impact of our feature selection on classification accuracy by way of three experimental scenarios that are compared alongside two other approaches: The All-features (i.e. classification without prior feature selection) and another hybrid technique combining the standard PSO algorithm with the Fuzzy k-NN technique (P-Fuzzy approach). In the first and second scenarios, we further divided the assessment criteria in terms of classification accuracy based on the choice of best features and those in terms of the different categories of the cervical cells. In the third scenario, we introduced new QH hybrid techniques, i.e. QPSO combined with other supervised learning methods, and compared the classification accuracy alongside our proposed Q-Fuzzy approach. Furthermore, we employed statistical approaches to establish qualitative agreement with regards to the feature selection in scenarios 1 and 3. The synergy between the QPSO and Fuzzy k-NN in the proposed Q-Fuzzy approach marginally improves classification accuracy as manifest in the reduction in number cell features, which is crucial for effective cervical cancer detection and diagnosis.

Subject Areas

computational intelligence; quantum hybrid intelligent systems; quantum machine learning; medical image processing; disease diagnosis; Fuzzy k-NN; Quantum-behaved PSO; cervical smear images; cancer detection

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