Classification of Breast DCE-MRI Images via Boosting and Deep Learning Based Stacking Ensemble Approach

Yurttakal, Ahmet Haşim; Erbay, Hasan; İkizceli, Türkan; Karaçavuş, Seyhan; Biçer, Cenker

doi:10.1007/978-3-030-51156-2_131

Cited by 2 publications

(1 citation statement)

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“…A variety of different classification techniques appear in the bibliography, including artificial neural networks (Twellmann et al, 2005), linear discriminant analysis (LDA) (Zheng et al, 2009), support vector machines (SVM) (Ayatollahi et al, 2020; Onan et al, 2016; Toçoğlu & Onan, 2021) and deep learning methods (Onan, 2020; Onan & Toçoğlu, 2021; Yurttakal et al, 2021) like deep artificial neural networks (Onan, 2020; Onan & Toçoğlu, 2021), recurrent neural networks (Antropova et al, 2018) and convolutional neural networks (Jiao et al, 2020). Additionally, ensemble learning approaches (Onan, 2016; Onan, 2017; Onan et al, 2016b; Onan et al, 2017; Onan & Toçoğlu, 2020) and fusion classifiers (Whitney et al, 2020) have been used.…”

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confidence: 99%

Dynamic contrast enhanced‐magnetic resonance imaging radiomics combined with a hybrid adaptiveneuro‐fuzzyinference system‐particle swarm optimization approach for breast tumour classification

et al. 2021

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The authors propose a method for breast dynamic contrast enhanced‐magnetic resonance imaging classification by combining radiomic texture analysis with a hybrid adaptive neuro‐fuzzy inference system (ANFIS)‐particle swarm optimization (PSO) classifier. The fast discrete curvelet transform is utilized as a decomposition scheme in multiple scales. The mean and entropy features extracted from the produced scheme are used as texture descriptors. Principal component analysis (PCA) involves reduction of the dimensionality of the initial feature set. The transformed feature vector is subsequently introduced to a hybrid ANFIS‐PSO classifier. The average overall classification power of the proposed hybrid ANFIS‐PSO classifier is comparatively assessed to that obtained using several classifiers (ANFIS, linear discriminant analysis, Naïve Bayes, artificial neural networks, random forest and support vector machine) by using the 70 training‐30 testing data ratio. The comparison performed highlights the superiority of the proposed methodology, thus underlying the potential of ANFIS‐PSO for the breast cancer diagnosis with a classification accuracy of 94%.

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