Breast cancer detection from histopathology images using modified residual neural networks

Gupta, Varun; Vasudev, Megha; Doegar, Amit; Sambyal, Nitigya

doi:10.1016/j.bbe.2021.08.011

Cited by 38 publications

(12 citation statements)

References 30 publications

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“…The limitation of their work is the binary classification of breast cancer, as multi-class classification was not performed. Gupta et al [ 48 ] proposed a modified residual networks-based DL solution for breast cancer classification, with an accuracy of 99%, and their work is limited to only binary class classification of breast cancer. The more recent work in breast cancer classification is presented in [ 49 , 50 , 51 , 52 ] that can further be improved in terms of accuracy.…”

Section: Discussionmentioning

confidence: 99%

Multi-Class Classification of Breast Cancer Using 6B-Net with Deep Feature Fusion and Selection Method

Umer

Sharif

Kadry³

et al. 2022

JPM

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Breast cancer has now overtaken lung cancer as the world’s most commonly diagnosed cancer, with thousands of new cases per year. Early detection and classification of breast cancer are necessary to overcome the death rate. Recently, many deep learning-based studies have been proposed for automatic diagnosis and classification of this deadly disease, using histopathology images. This study proposed a novel solution for multi-class breast cancer classification from histopathology images using deep learning. For this purpose, a novel 6B-Net deep CNN model, with feature fusion and selection mechanism, was developed for multi-class breast cancer classification. For the evaluation of the proposed method, two large, publicly available datasets, namely, BreaKHis, with eight classes containing 7909 images, and a breast cancer histopathology dataset, containing 3771 images of four classes, were used. The proposed method achieves a multi-class average accuracy of 94.20%, with a classification training time of 226 s in four classes of breast cancer, and a multi-class average accuracy of 90.10%, with a classification training time of 147 s in eight classes of breast cancer. The experimental outcomes show that the proposed method achieves the highest multi-class average accuracy for breast cancer classification, and hence, the proposed method can effectively be applied for early detection and classification of breast cancer to assist the pathologists in early and accurate diagnosis of breast cancer.

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Section: Discussionmentioning

confidence: 99%

Multi-Class Classification of Breast Cancer Using 6B-Net with Deep Feature Fusion and Selection Method

Umer

Sharif

Kadry³

et al. 2022

JPM

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“…For bioimage classification, an ensemble model containing CNNs as a base classifier and a sum rule for final decision-making has been proposed [23]. A modified residual neural network [24] has been proposed for breast cancer detection from histopathology images using modified ResNet34 and modified ResNet50 models. A Stochastic Dilated Residual Ghost (SDRG) method [25] has been proposed for cancer detection from breast histopathology images.…”

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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“…If the pathologist needs a clearer view of the fine structure of the lesion, the lesion can be moved to the center of the view field and switched to high magnification microscopy for further analysis [8]. However, the observation of the entire histopathological examination process leaves the following drawbacks: the diagnostic results are highly subjective and difficult to describe quantitatively; because of the workload of physicians, the section information can be easily missed out during prolonged examinations; and the diagnostic process is difficult to use big data technologies [9]. As a result, there is an urgent need to address those problems more effectively.…”

Section: Introductionmentioning

confidence: 99%

EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation

Hu¹,

Li²,

Li³

et al. 2022

Preprint

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Background and purpose: Colorectal cancer has become the third most common cancer worldwide, accounting for approximately 10% of cancer patients. Early detection of the disease is important for the treatment of colorectal cancer patients. Histopathological examination is the gold standard for screening colorectal cancer. However, the current lack of histopathological image datasets of colorectal cancer, especially enteroscope biopsies, hinders the accurate evaluation of computer-aided diagnosis techniques. Methods: A new publicly available Enteroscope Biopsy Histopathological H&E Image Dataset (EBHI) is published in this paper. To demonstrate the effectiveness of the EBHI dataset, we have utilized several machine learning, convolutional neural networks and novel transformer-based classifiers for experimentation and evaluation, using an image with a magnification of 200×. Results: Experimental results show that the deep learning method performs well on the EBHI dataset. Traditional machine learning methods achieve maximum accuracy of 76.02% and deep learning method achieves a maximum accuracy of 95.37%. Conclusion: To the best of our knowledge, EBHI is the first publicly available colorectal histopathology enteroscope biopsy dataset with four magnifications and five types of images of tumor differentiation stages, totaling 5532 images. We believe that EBHI could attract researchers to explore new classification algorithms for the automated diagnosis of colorectal cancer, which could help physicians and patients in clinical settings.

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Breast cancer detection from histopathology images using modified residual neural networks

Cited by 38 publications

References 30 publications

Multi-Class Classification of Breast Cancer Using 6B-Net with Deep Feature Fusion and Selection Method

Multi-Class Classification of Breast Cancer Using 6B-Net with Deep Feature Fusion and Selection Method

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

EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation

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