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Detection of skin cancer through hybrid color features and soft voting ensemble classifier

  • S.I. : Low Resource Machine Learning Algorithms (LR-MLA)
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Abstract

Recent diagnostic observations have raised concerns regarding malignancy issues in untreated skin cancer. Over the last two decades, dermoscopy has led to non-invasive clinical diagnosis of cancer types, typically Melanoma, Basal Cell Carcinoma (BCC), and Squamous Cell Carcinoma (SCC). Raising concerns coupled with clinical (image) data motivated this study which focuses on a Machine Learning (ML) based approach to properly classify the skin cancer into one of the three types mentioned earlier. In this regard, the study also uses a unique image pre-processing network which cleans and processes the images prior to injection into the ML models. In this study, the image pre-processing network primarily comprises of Gaussian noise removal, blackhat operation, and Otsu segmentation. Post processing, a novel Hybrid Color Features (HCF) algorithm has been used for feature extraction in terms of texture, shape and color features. Outlier handling was done using the z-score rescaling approach. In comparison to the traditional ML approach, it had been observed that the novel soft voting ensemble classifier (SVEC) exhibited better results. The SVEC along with boosting fetched an accuracy of 95.5%, 96.3%, and 96.7% for Melanoma, BCC, and SCC skin cancers, respectively. Peer comparison with prior ML initiatives further confirmed the novelty of the SVEC approach.

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Data availability

The authors hereby declare that the data associated with study is publicly available.

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Authors and Affiliations

  1. Dept. of Computer Science and Engineering, Guru Nanak Institute of Technology, Kolkata, India

    Ananjan Maiti, Mahamuda Sultana & Suman Bhattacharya

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  1. Ananjan Maiti
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  2. Mahamuda Sultana
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  3. Suman Bhattacharya
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Correspondence to Mahamuda Sultana.

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Maiti, A., Sultana, M. & Bhattacharya, S. Detection of skin cancer through hybrid color features and soft voting ensemble classifier. Innovations Syst Softw Eng (2022). https://doi.org/10.1007/s11334-022-00498-8

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