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AFDL: a new adaptive fuzzy dictionary learning for medical image classification

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Abstract

Sparse coding allows the representation of complex data as a linear combination of basis sparse vectors (alternatively called atoms or codewords), a collection of which constitutes a dictionary. Dictionary learning is a learning process aimed at finding a small number of optimal basis vectors for a more accurate representation of the original data. The existing dictionary learning methods do not address the inherent uncertainty of the input data in their learning processes. To compensate for the uncertainty, and to obtain a flexible and effective learning system, we introduce a new adaptive fuzzy dictionary learning (AFDL) method for image classification purposes. The new method iteratively alternates between sparse coding based on a given dictionary and an adaptive fuzzy dictionary learning approach to learn (improve) dictionary atoms. The adjustability of the dictionary and coefficients vectors, in this method, provide us a more accurate and straight representation of input data. AFDL was applied on magnetic resonance images from the cancer image archive datasets, for medical image classification of cancer tumors. Finally, the overall experimental results clearly show that our approach outperforms its rival techniques in terms of accuracy, sensitivity, and specificity. Convergence speed in the experimental results shows that AFDL can achieve its acceptable precision in a reasonable time.

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Majid Ghasemi & Arash Sharifi

  2. Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran

    Manoochehr Kelarestaghi & Farshad Eshghi

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  1. Majid Ghasemi
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  2. Manoochehr Kelarestaghi
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  3. Farshad Eshghi
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Correspondence to Manoochehr Kelarestaghi.

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Ghasemi, M., Kelarestaghi, M., Eshghi, F. et al. AFDL: a new adaptive fuzzy dictionary learning for medical image classification. Pattern Anal Applic 24, 145–164 (2021). https://doi.org/10.1007/s10044-020-00909-1

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