T2-FDL: A robust sparse representation method using adaptive type-2 fuzzy dictionary learning for medical image classification
作者：
Highlights：
• A robust sparse representation method is proposed for image classification.
• A type-2 fuzzy system is used to learn dictionary items efficiently.
• The robust dictionary learning method can reduce the effect of uncertainties.
• Two well-known brain tumor image databases are used to evaluate the proposed method.
• Substantial improvement in terms of accuracy, sensitivity, and specificity observed.
摘要
•A robust sparse representation method is proposed for image classification.•A type-2 fuzzy system is used to learn dictionary items efficiently.•The robust dictionary learning method can reduce the effect of uncertainties.•Two well-known brain tumor image databases are used to evaluate the proposed method.•Substantial improvement in terms of accuracy, sensitivity, and specificity observed.
论文关键词：Robust sparse representation,Dictionary learning,Uncertainty,Near-optimal dictionary,Type-2 fuzzy learning,Medical diagnosis
论文评审过程：Received 1 May 2019, Revised 28 April 2020, Accepted 29 April 2020, Available online 4 May 2020, Version of Record 20 May 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113500
