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D3FC: deep feature-extractor discriminative dictionary-learning fuzzy classifier for medical imaging

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Abstract

Providing accurate and speedy diagnosis and, in turn, treatment, automated medical image analysis plays a significant role in survival rate improvement. Inherent different kinds of uncertainties and complexities prove machine learning-based, particularly dictionary-learning-based classification approaches, very promisingly. This work concerns class-specific fuzzy discriminative dictionary learning using deep features on the continuum of our machine-learning-based medical image classifiers’ evolution path. In D3FC, a deep autoencoder generates a more relevant, representative, and compact features set. The distinctive-hidden information and inherent complexity and uncertainty of medical images are addressed using fuzzy-discriminative terms in the optimization function, simultaneously improving the inter-class-representation distance and intra-class-representation similarity. A comprehensive set of experiments on cancer tumor images from three different databases shows the outperformance of D3FC over related state-of-the-art competitions in accuracy, sensitivity, specificity, precision, convergence speed, and noise resilience. The meaningfulness of the experiments’ results is statistically verified.

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Authors and Affiliations

  1. Department of Computer Engineering, Farsan Branch, Islamic Azad University, Farsan, 88137-33395, Iran

    Majid Ghasemi & Arash Sharifi

  2. Department of Electrical and Computer Engineering, Kharazmi University, Tehran, 15719-14911, 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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  4. Arash Sharifi
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Correspondence to Manoochehr Kelarestaghi.

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Ghasemi, M., Kelarestaghi, M., Eshghi, F. et al. D3FC: deep feature-extractor discriminative dictionary-learning fuzzy classifier for medical imaging. Appl Intell 52, 7201–7217 (2022). https://doi.org/10.1007/s10489-021-02781-w

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