Breast cancer detection using an ensemble deep learning method

Das, Abhishek; Mohanty, Mihir Narayan; Mallick, Pradeep Kumar; Tiwari, Prayag; Muhammad, Khan; Zhu, Hongyin

doi:10.1016/j.bspc.2021.103009

Cited by 69 publications

(25 citation statements)

References 48 publications

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“…Different classifiers can capture different information and therefore, ensemble classifiers may result in great/better accuracy. Furthermore, ensemble learning methods are widely used in different medical image classification tasks [26]. In [24], Kumar et al suggested that different CNN classifiers can learn various levels of semantic image representation.…”

Section: Ensemble Learningmentioning

confidence: 99%

“…In their work, [25] Kassani et al employ a model which ensembles pretrained VGG19, MobileNet, and DenseNet to detect cancerous regions from the breast histology images. Das et al [26] feed the wavelet transformed breast histology images to a model which ensembles three different CNN structures. Kundu et al [27] design a network, which is composed of LeNet, ResNet-18 and DenseNet 121, to detect pneumonia from chest X-Ray images.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Colorectal Polyps from Histopathological Images using Ensemble of ConvNeXt Variants

Tasdemir

Akay

Doğan

et al. 2022

Preprint

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In this work, we propose a computer-aided diagnosis system for automatic classification of adenomatous polyps from colonic histological images. The proposed method employs an ensemble of ConvNeXt variants, which is one of the most recent and prominent convolution based deep learning architectures. Comprehensive experiments show that classification performance of the proposed method outperforms the state-of-the-art Deep CNN models on our dataset, with a 95% accuracy. It achieves 91.1% and 90% classification accuracy respectively on EBHI and UniToPatho datasets, which shows that the model can generalize well for other datasets as well. Furthermore, we investigate the effect of stain normalization preprocessing on the classification accuracy. While the other methods are sensitive to the normalization, the proposed method retains its accuracy levels under the variations, which also indicates a high generalization ability. Lastly, we use the Grad-Cam method and show that the proposed model gives more attention to the regions where the cancer indicators potentially reside.

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Section: Ensemble Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of Colorectal Polyps from Histopathological Images using Ensemble of ConvNeXt Variants

Tasdemir

Akay

Doğan

et al. 2022

Preprint

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“…Mitotic cell detection for breast cancer classification has been proposed using two CNNs connected in parallel [20]. Application of empirical wavelet transform (EWT), as well as variational mode decomposition (VMD) as preprocessors followed by ensemble of three CNNs and MLP [21], has been proposed for breast cancer detection from histopathology images. That work also used gene data from breast cancer for image conversion using the DeepInsight framework [22].…”

Section: Literature Surveymentioning

confidence: 99%

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

Das

Mohanty

2022

ACI

Self Cite

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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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“…This method is used in applications of common activities and places as well. For instance, in a method that places products on synthetic backgrounds of shelves on a grocery object localization task [31], detection of pedestrian [25,27], cyclists [32], vehicles [26] and breast cancer [33], classification of birds and aerial vehicles [22], and synthetic Magnetic Resonance Imaging (MRI) [34]. However, generated images sometimes present a lack of realism, making models trained on synthetic data perform poorly on real images [24].…”

Section: Synthetic and Real Datamentioning

confidence: 99%

Blending Colored and Depth CNN Pipelines in an Ensemble Learning Classification Approach for Warehouse Application Using Synthetic and Real Data

et al. 2021

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Electric companies face flow control and inventory obstacles such as reliability, outlays, and time-consuming tasks. Convolutional Neural Networks (CNNs) combined with computational vision approaches can process image classification in warehouse management applications to tackle this problem. This study uses synthetic and real images applied to CNNs to deal with classification of inventory items. The results are compared to seek the neural networks that better suit this application. The methodology consists of fine-tuning several CNNs on Red–Green–Blue (RBG) and Red–Green–Blue-Depth (RGB-D) synthetic and real datasets, using the best architecture of each domain in a blended ensemble approach. The proposed blended ensemble approach was not yet explored in such an application, using RGB and RGB-D data, from synthetic and real domains. The use of a synthetic dataset improved accuracy, precision, recall and f1-score in comparison with models trained only on the real domain. Moreover, the use of a blend of DenseNet and Resnet pipelines for colored and depth images proved to outperform accuracy, precision and f1-score performance indicators over single CNNs, achieving an accuracy measurement of 95.23%. The classification task is a real logistics engineering problem handled by computer vision and artificial intelligence, making full use of RGB and RGB-D images of synthetic and real domains, applied in an approach of blended CNN pipelines.

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Breast cancer detection using an ensemble deep learning method

Cited by 69 publications

References 48 publications

Classification of Colorectal Polyps from Histopathological Images using Ensemble of ConvNeXt Variants

Classification of Colorectal Polyps from Histopathological Images using Ensemble of ConvNeXt Variants

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

Blending Colored and Depth CNN Pipelines in an Ensemble Learning Classification Approach for Warehouse Application Using Synthetic and Real Data

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