Residual learning based CNN for breast cancer histopathological image classification

Gour, Mahesh; Jain, Sweta; Kumar, Tapan

doi:10.1002/ima.22403

Cited by 184 publications

(91 citation statements)

References 30 publications

(43 reference statements)

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“…[61], Sudharshan et al [38], and Gour et al [62]. This is the first attempt that we use AnoGAN for screening discriminative patches to deal with the mislabeled patches.…”

Section: Discussionmentioning

confidence: 99%

Classification of Breast Cancer Histopathological Images Using Discriminative Patches Screened by Generative Adversarial Networks

Man

Yang

2020

IEEE Access

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Computer-aided diagnosis (CAD) systems of breast cancer histopathological images automated classification can help reduce the manual observation workload of pathologists. In the classification of breast cancer histopathology images, due to the small number and high-resolution of the training samples, the patch-based image classification methods have become very necessary. However, adopting a patches-based classification method is very challenging, since the patch-level datasets extracted from whole slide images (WSIs) contain many mislabeled patches. Existing patch-based classification methods have paid little attention to addressing the mislabeled patches for improving the performance of classification. To solve this problem, we propose a novel approach, named DenseNet121-AnoGAN, for classifying breast histopathological images into benign and malignant classes. The proposed approach consists of two major parts: using an unsupervised anomaly detection with generative adversarial networks (AnoGAN) to screen mislabeled patches and using densely connected convolutional network (DenseNet) to extract multi-layered features of the discriminative patches. The performance of the proposed approach is evaluated on the publicly available BreaKHis dataset using 5-fold cross validation. The proposed DenseNet121-AnoGAN can be better suited to coarse-grained highresolution images and achieved satisfactory classification performance in 40X and 100X images. The best accuracy of 99.13% and the best F 1 score of 99.38% have been obtained at the image level for the 40X magnification factor. We have also investigated the performance of AnoGAN on the other classification networks, including AlexNet, VGG16, VGG19, and ResNet50. Our experiments show that whether it is at the patient-level accuracy or at the image-level accuracy, the classification networks with AnoGAN have provided better performance than the classification networks without AnoGAN. INDEX TERMS Breast cancer histopathological images, densely connected convolutional networks, discriminative patches, generative adversarial networks, image classification

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“…[61], Sudharshan et al [38], and Gour et al [62]. This is the first attempt that we use AnoGAN for screening discriminative patches to deal with the mislabeled patches.…”

Section: Discussionmentioning

confidence: 99%

Classification of Breast Cancer Histopathological Images Using Discriminative Patches Screened by Generative Adversarial Networks

Man

Yang

2020

IEEE Access

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“…In [87], a ResHist model is designed, which is a residual learning-based 152-layered CNN to classify the histopathological images of breast cancer. In the experiment, histopathological images are first augmented and the ResHist model is trained end-to-end on the augmented dataset in a supervised learning manner.…”

Section: Related Work Of Breakhis In 2020mentioning

confidence: 99%

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

et al. 2020

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Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.

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“…A binary classification model using a residual learning CNN approach was proposed to learn discriminative features from histopathological images [ 12 ]. The algorithm achieved 84.34% and 92.52% classification accuracy without and with augmentation preprocess in the network on the BreaKHis dataset respectively.…”

Section: Related Workmentioning

confidence: 99%

Spectral–Spatial Features Integrated Convolution Neural Network for Breast Cancer Classification

Mewada

Patel

Hassaballah

et al. 2020

Sensors

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Cancer identification and classification from histopathological images of the breast depends greatly on experts, and computer-aided diagnosis can play an important role in disagreement of experts. This automatic process has increased the accuracy of the classification at a reduced cost. The advancement in Convolution Neural Network (CNN) structure has outperformed the traditional approaches in biomedical imaging applications. One of the limiting factors of CNN is it uses spatial image features only for classification. The spectral features from the transform domain have equivalent importance in the complex image classification algorithm. This paper proposes a new CNN structure to classify the histopathological cancer images based on integrating the spectral features obtained using a multi-resolution wavelet transform with the spatial features of CNN. In addition, batch normalization process is used after every layer in the convolution network to improve the poor convergence problem of CNN and the deep layers of CNN are trained with spectral–spatial features. The proposed structure is tested on malignant histology images of the breast for both binary and multi-class classification of tissue using the BreaKHis Dataset and the Breast Cancer Classification Challenge 2015 Datasest. Experimental results show that the combination of spectral–spatial features improves classification accuracy of the CNN network and requires less training parameters in comparison with the well known models (i.e., VGG16 and ALEXNET). The proposed structure achieves an average accuracy of 97.58% and 97.45% with 7.6 million training parameters on both datasets, respectively.

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Residual learning based CNN for breast cancer histopathological image classification

Cited by 184 publications

References 30 publications

Classification of Breast Cancer Histopathological Images Using Discriminative Patches Screened by Generative Adversarial Networks

Classification of Breast Cancer Histopathological Images Using Discriminative Patches Screened by Generative Adversarial Networks

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Spectral–Spatial Features Integrated Convolution Neural Network for Breast Cancer Classification

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