Intelligent skin cancer detection using enhanced particle swarm optimization
Introduction
Melanoma is an aggressive type of skin cancer that can spread to other organs. Automatic and early diagnosis of melanoma is essential for administering effective treatment and increasing survival chances. Since medical skin cancer diagnosis employs the Asymmetry, Border, Colour, Diameter and Enlargement (ABCDE) guideline, the extraction and identification of such discriminative and significant morphological characteristics play a crucial role in attaining accurate diagnosis rates. However, it is still a challenging task for the retrieval of such distinguishing attributes, owing to the fine-grained variability in the appearance of benign and cancerous skin lesions [1].
This research aims to deal with the above challenge by proposing an enhanced Particle Swarm Optimization (PSO) algorithm for discriminative feature selection in skin cancer diagnosis using dermoscopic images. The main motivations of this research are as follows. Evolutionary algorithms possess powerful search capabilities, and have been widely used for solving various feature selection challenges. Owing to the comparatively simple underlying concepts and relatively few user-defined parameters, PSO has been widely studied for feature selection tasks. Since PSO has a weak exploration capability, and its search process is likely to be trapped in local optima when dealing with multimodal or complex optimization problems, new PSO variants with superior explorative capabilities are required. Therefore, we propose an enhanced PSO model in this research.
Specifically, our proposed PSO model incorporates the subswarm concept, food and enemy signals, attraction and flee operations, mutation-based local exploitation, and diverse matrix representations to mitigate premature convergence of the original PSO algorithm. It shows a great superiority over other methods for the identification of the most significant characteristics of benign and malignant lesion images to facilitate subsequent skin cancer classification. The proposed skin cancer detection system consists of five key stages, i.e. pre-processing, skin lesion segmentation, feature extraction, PSO-based feature selection and classification. The overall system architecture is illustrated in Fig. 1.
The key contributions of this research, which focus on PSO-based feature selection, are as follows.
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The proposed PSO model first of all divides the original population into two subswarms. Then, two swarm leaders with competitive fitness scores but low position proximity are identified. Each leader leads one subswarm-based search for discriminative lesion feature selection. Since the subswarm-based search is more likely to explore distinctive search regions owing to the low position correlation between the two leaders, it reduces the probability of being trapped in local optima and increases the chances of finding the global optimum.
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A number of attraction and enemy signals are used for velocity updating in each subswarm. The proposed PSO model enables each particle to follow the leader (i.e. food attraction) and avoid unpromising solutions (i.e. enemies) partially (i.e. in randomly selected sub-dimensions) as well as fully (i.e. in each dimension) to diversify the search process. Three random walks, i.e. Gaussian, Cauchy, and Levy distributions, are used to further enhance the best subswarm solution and to increase exploitation. A dynamic matrix representation of the swarm is also utilized during the search process to increase search diversity. The proposed algorithm shows great efficiency in optimal feature selection for melanoma classification, as well as solving unimodal and multimodal benchmark problems in comparison with other search methods. It is also among the top performers for skin cancer detection in comparison with related research studies reported in the literature.
The paper is organised as follows. Section 2 presents the related studies on skin cancer detection, enhanced PSO variants and diverse evolutionary algorithm-based feature selection techniques. Section 3 presents the proposed PSO model with mutation-based local exploitation as well as attraction and flee based global exploration. A detailed evaluation of the proposed algorithm and other classical methods using skin lesion data sets and other benchmark problems is presented in Section 4. Concluding remarks and suggestions for further research are provided in Section 5.
Section snippets
Related work
In this section, we discuss the related work on computerized skin cancer diagnosis, diverse variants of the PSO algorithm, and evolutionary algorithm-based feature selection methods.
The proposed skin cancer detection system
We propose an intelligent system for benign and malignant skin lesion classification. The proposed system consists of five key stages, i.e. pre-processing, skin lesion segmentation, feature extraction, PSO-based feature optimization and classification. Each key stage, especially the feature selection process, is explained comprehensively, as follows.
Evaluation
To evaluate the proposed PSO variant, we implement several classical search methods for comparison, i.e., PSO [38], Bat Algorithm (BA) [40], Harmony Search (HS) [41], GA [42], Dragonfly Algorithm (DA) [43], Flower Pollination Algorithm (FPA) [44], Moth-Flame Optimization (MFO) [45], Artificial Bee Colony (ABC) [46], Cultural Algorithm (CA) [47], and BBPSO [48]. Several advanced PSO variants are implemented for comparison, including DNLPSO [15], ELPSO [16], AGPSO [17], ThBPSO [26], MS-PSO [27],
Conclusions
In this research, we have described skin lesion classification using PSO-based feature optimization. The proposed PSO model integrates diverse alternative velocity updating strategies in the subswarms to enable a wider exploration of the search space. Two remote swarm leaders have been employed to lead the subswarm-based search to explore distinctive regions. Probability distributions and dynamic matrix representation have also been utilized to increase diversification. The proposed PSO model
Acknowledgements
We appreciate the support for this research received from the European Union (EU) sponsored (Erasmus Mundus) cLINK (Centre of excellence for Learning, Innovation, Networking and Knowledge) project (EU Grant No. 2645).
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