The Xception model: A potential feature extractor in breast cancer histology images classification

Sharma, Shallu; Kumar, Sumit

doi:10.1016/j.icte.2021.11.010

Cited by 77 publications

(28 citation statements)

References 21 publications

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“…As illustrated in Table 6, IRv2-CXL achieved better results than the 16 models included in the table in three of the four magnifications factors. In the 40× magnification factor, the proposed model outperformed almost all the other models by a considerable margin, the only close ones being Sharma and Kumar [47] with 96.25% and Kumar et al [48] with 94.11%. IRv2-CXL attained a 95.84% accuracy in the 100× magnification factor, only outdone by the 96.25% achieved by Sharma and Kumar [47] with a 0.41% margin.…”

Section: Discussionmentioning

confidence: 86%

“…In the 40× magnification factor, the proposed model outperformed almost all the other models by a considerable margin, the only close ones being Sharma and Kumar [47] with 96.25% and Kumar et al [48] with 94.11%. IRv2-CXL attained a 95.84% accuracy in the 100× magnification factor, only outdone by the 96.25% achieved by Sharma and Kumar [47] with a 0.41% margin. The 200× magnification factor led to the best individual magnification result of the proposed model.…”

Section: Discussionmentioning

confidence: 86%

See 1 more Smart Citation

Classification of Breast Tumors Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods

Abbasniya

Sheikholeslamzadeh²,

Nasiri³

et al. 2022

Computers and Electrical Engineering

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Section: Discussionmentioning

confidence: 86%

Section: Discussionmentioning

confidence: 86%

Classification of Breast Tumors Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods

Abbasniya

Sheikholeslamzadeh²,

Nasiri³

et al. 2022

Computers and Electrical Engineering

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“…Meanwhile, confusion matrices achieved by SoACNet are also drawn in Figure 4. Moreover, we also compare SoACNet with five representative CNN-related models [14,20,23,25,30] in terms of precision and recall indexes, and the comparative results are tabled in Table 5. As shown in this table, SoACNet outperforms the five models on all the four data sets under the precision index.…”

Section: Precision Recall and F1-score Resultsmentioning

confidence: 99%

“…Generally, CNN-based classification methods for breast cancer histopathology images can mainly be divided into the following two streams. Firstly, some researchers utilize newly constructed or representative CNNs as feature extractors to capture deep features, and then employ traditional classifiers to distinguish pathological images based on extracted CNN features, belonging to non-end-to-end models [10,12,[14][15][16][17][18][19][20]. For example, Spanhol et al [12] comprehensively evaluate the pre-trained CaffeNet model on breast cancer classification task, and results demonstrate the superior performance over traditional hand-crafted texture descriptors and task-specific CNNs under certain conditions.…”

Section: Introductionmentioning

confidence: 99%

Second‐order asymmetric convolution network for breast cancer histopathology image classification

Hou

Wang

et al. 2022

Journal of Biophotonics

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Recently, convolutional neural networks (CNNs) have been widely utilized for breast cancer histopathology image classification. Besides, research works have also convinced that deep high-order statistic models obviously outperform corresponding first-order counterparts in vision tasks. Inspired by this, we attempt to explore global deep high-order statistics to distinguish breast cancer histopathology images. To further boost the classification performance, we also integrate asymmetric convolution into the second-order network and propose a novel second-order asymmetric convolution network (SoACNet). SoACNet adopts a series of asymmetric convolution blocks to replace each stand square-kernel convolutional layer of the backbone architecture, followed by a global covariance pooling to compute second-order statistics of deep features, leading to a more robust representation of histopathology images. Extensive experiments on the public BreakHis dataset demonstrate the effectiveness of SoACNet for breast cancer histopathology image classification, which achieves competitive performance with the state-ofthe-arts.

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“…In recent work, the combination of the attention technique and residual CNN model [26] has been utilized for breast cancer detection. A combination of the Xception model as feature extractor and radial basis function (RBF) kernel-based SVM as classifier [27] has been proposed for breast cancer detection from histopathology images. The authors have also studied the effect of magnification factors on performance.…”

Section: Literature Surveymentioning

confidence: 99%

Design of ensemble recurrent model with stacked fuzzy ARTMAP for breast cancer detection

Das

Mohanty

2022

ACI

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PurposeIn time and accurate detection of cancer can save the life of the person affected. According to the World Health Organization (WHO), breast cancer occupies the most frequent incidence among all the cancers whereas breast cancer takes fifth place in the case of mortality numbers. Out of many image processing techniques, certain works have focused on convolutional neural networks (CNNs) for processing these images. However, deep learning models are to be explored well.Design/methodology/approachIn this work, multivariate statistics-based kernel principal component analysis (KPCA) is used for essential features. KPCA is simultaneously helpful for denoising the data. These features are processed through a heterogeneous ensemble model that consists of three base models. The base models comprise recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The outcomes of these base learners are fed to fuzzy adaptive resonance theory mapping (ARTMAP) model for decision making as the nodes are added to the F_2ˆa layer if the winning criteria are fulfilled that makes the ARTMAP model more robust.FindingsThe proposed model is verified using breast histopathology image dataset publicly available at Kaggle. The model provides 99.36% training accuracy and 98.72% validation accuracy. The proposed model utilizes data processing in all aspects, i.e. image denoising to reduce the data redundancy, training by ensemble learning to provide higher results than that of single models. The final classification by a fuzzy ARTMAP model that controls the number of nodes depending upon the performance makes robust accurate classification.Research limitations/implicationsResearch in the field of medical applications is an ongoing method. More advanced algorithms are being developed for better classification. Still, the scope is there to design the models in terms of better performance, practicability and cost efficiency in the future. Also, the ensemble models may be chosen with different combinations and characteristics. Only signal instead of images may be verified for this proposed model. Experimental analysis shows the improved performance of the proposed model. This method needs to be verified using practical models. Also, the practical implementation will be carried out for its real-time performance and cost efficiency.Originality/valueThe proposed model is utilized for denoising and to reduce the data redundancy so that the feature selection is done using KPCA. Training and classification are performed using heterogeneous ensemble model designed using RNN, LSTM and GRU as base classifiers to provide higher results than that of single models. Use of adaptive fuzzy mapping model makes the final classification accurate. The effectiveness of combining these methods to a single model is analyzed in this work.

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The Xception model: A potential feature extractor in breast cancer histology images classification

Cited by 77 publications

References 21 publications

Classification of Breast Tumors Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods

Classification of Breast Tumors Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods

Second‐order asymmetric convolution network for breast cancer histopathology image classification

Design of ensemble recurrent model with stacked fuzzy ARTMAP for breast cancer detection

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