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Dual feature selection and rebalancing strategy using metaheuristic optimization algorithms in X-ray image datasets

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Abstract

The imbalance and multi-dimension are two common problems in the medical image datasets, which affect the performances of the image processing procedures. The traditional methods to solve these two problems are notoriously difficult. Accordingly, this work employed metaheuristic methods to optimize the rebalancing process of the imbalanced class distribution for further use in the feature selection procedure for dimensionality reduction for the medical X-ray image datasets. Different metaheuristic algorithms were used to maximize the parameter values of the rebalancing and feature selection phases to preprocess the datasets. The proposed work devised a multi-objective optimization strategy in the process of the metaheuristic algorithms search to solve the problem of dual imbalanced dataset and feature selection. Afterward, a comparative study of the proposed optimized approach with the conventional methods was conducted to evaluate the proposed method performance. The results established the superiority of the proposed method to overcome the imbalanced and multi-dimensional problem. The proposed method generated a reasonable number of minority class samples and selected a sensible subset of features to ultimately obtain a very extraordinary accuracy with great credibility from a negative value of kappa and a false high accuracy. It produced higher credibility and correctness classification performance in the practical problem of medical X-ray images compared to other algorithms. Feature selection with Random-SMOTE (RSMOTE) using the self-adaptive Bat algorithm is superior to the optimization using particle swarm optimization. The proposed method using the Bat algorithm achieved 94.6% classification accuracy with 0.883 Kappa value using the lung X-ray first dataset.

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Acknowledgements

The authors are thankful for the financial support from the Research Grants, “Temporal Data Stream Mining by Using Incrementally Optimized Very Fast Decision Forest (iOVFDF), Grant no. MYRG2015-00128-FST”, “Improving the Protein-Ligand Scoring Function for Molecular Docking by Fuzzy Rule-based Machine Learning Approaches, Grant no. MYRG2016-00217-FST”, and “A Scalable Data Stream Mining Methodology: Stream-based Holistic Analytics and Reasoning in Parallel, Grant no. FDCT/126/2014/A3”, offered by the University of Macau and Macau FDCT respectively. We thank also Ms. Dantong Wang, our former MSc student, for her technical contribution to this paper as well as a geat appreciation is directed to Dr. Li Tengyue (Department of Computer and Information Science, University of Macau, Taipa, Macau SAR) for her helps.

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Authors and Affiliations

  1. Data & Analysing Platform Department of Consumer BG, Huawei Technologies CO. LTD, Shenzhen, China

    Jinyan Li

  2. Data Analytics and Collaborative Computing Laboratory, Department of Computer and Information Science, University of Macau, Taipa, Macau, SAR

    Simon Fong

  3. Medical Imaging Department, First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, 510405, Guangdong, China

    Lian-sheng Liu

  4. Department of Information Technology, Techno India College of Technology, Rajarhat, Kolkata, India

    Nilanjan Dey

  5. Depatrment of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt

    Amira S. Ashour

  6. Faculty of Sciences and Environment, Dunarea de Jos University of Galati, 47 Domneasca St., 800008, Galati, Romania

    Luminița Moraru

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  1. Jinyan Li
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Correspondence to Amira S. Ashour.

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Li, J., Fong, S., Liu, Ls. et al. Dual feature selection and rebalancing strategy using metaheuristic optimization algorithms in X-ray image datasets. Multimed Tools Appl 78, 20913–20933 (2019). https://doi.org/10.1007/s11042-019-7354-5

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