Breast Cancer Classification From Histopathological Images Using Resolution Adaptive Network

Zhang, Can; Gao, Shaoshuai

doi:10.1109/access.2022.3163822

Cited by 26 publications

(11 citation statements)

References 45 publications

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“…The HI classification for BC classification has reported average classification accuracy of 84.89% using AlexNet [65], 93.5% using GoogLeNet, 94.13% using VGGNets and 94.35% using ResNet for image level classification on BreaKHis dataset [66]. Binary classification of BC is done with the existing Deep networks such as Resnet101 and DenseNet121 has reported classification accuracy of 91.43 % and 96.74% respectively using BreaKHis dataset, while it is 91.53% and 96.38% respectively using BACH-2018 dataset [67]. The DenseNet121+GAN has reported 99.13% accuracy [68].…”

Section: A Deep Convolutional Neural Network (Dcnn) Based Approachmentioning

confidence: 99%

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GARL-Net: Graph Based Adaptive Regularized Learning Deep Network for Breast Cancer Classification

et al. 2023

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Across the globe, women suffer from breast cancer fatal disease. It is arising surprisingly due to a lack of awareness among them and the inconvenient reach of diagnostic systems. Many computeraided diagnostic systems have been developed for the detection of cancer. These systems have quite lower performance, so more accurate diagnosis is the need of the time to save the life of human beings. For large and imbalanced image datasets, efficient learning of the network is very important to detect and classify breast cancer more accurately. In this paper, a graph based adaptive regularized learning of deep network (GARL-Net) is proposed for more accurate breast cancer classification. Transfer learning is used for training the backbone network DenseNet121. furthermore, fine tuning of backbone network is followed by the estimated improved loss function. The improved loss function is actually graph based adaptively regularized complement cross entropy loss. The SoftMax cross entropy in itself is not sufficient to classify image samples accurately, so complement entropy technique is incorporated with the cross-entropy loss to overcome the misclassification issue. Further, by adaptive scaling of regularization term with spatial graph Laplacian basis used to adaptively penalize the complement cross entropy loss for improving the learning of the network. The performance of the proposed method is evaluated using BreakHis and BACH 2018 histopathology image datasets and outperforms the existing state-of-the-art methods and achieved 99.00% of precision, 99.40% of recall, 99.20 % of F-1score, and 99.49% of accuracy for binary classification of breast cancer image samples of BreakHis dataset. INDEX TERMSDeep learning, breast cancer image classification, complement cross entropy, transfer learning, graph Laplacian basis regularization, computer aided diagnostic.

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Section: A Deep Convolutional Neural Network (Dcnn) Based Approachmentioning

confidence: 99%

“…al., where GAN and DenseNet121 are combined to perform anomaly detection. The combination makes system bulky and computationally inefficient and reported low accuracy [67].…”

Section: E Limitations Of Existing Workmentioning

confidence: 99%

GARL-Net: Graph Based Adaptive Regularized Learning Deep Network for Breast Cancer Classification

et al. 2023

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“…In this ablation study, results of the proposed MSF model are compared with state-of-theart ResHist [7], Kernelized Weighted Model (KWM) [32], multi-instance support vector machine (pdMISVM) [42], Resolution Adaptive Network (RAN) [43], MA-MIDN [44], DBLCNN [45], GLCM [46], and AE + Siamese Network (AE+SN) [47] The MSF model has f-measure of 98.07% which is highest as compared to other models. Kernelized weighted extreme learning model [32] has f-measure of 89.90% which is the [43] has accuracy 97.91%. Kernelized weighted extreme learning model [32] has the lowest accuracy of 87.14% on BreaKHis dataset.…”

Section: Comparison With Existing Studiesmentioning

confidence: 99%

MSF-Model: Multi-Scale Feature Fusion-Based Domain Adaptive Model for Breast Cancer Classification of Histopathology Images

et al. 2022

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One of the most common causes of mortality for women globally is breast cancer. Early breast cancer identification could make it possible for people to receive the appropriate treatment to save their lives and return to their routine lives. Breast cancer diagnosis by histopathology is referred to as the gold standard. In recent years, convolutional neural network-based techniques are used for breast cancer classification. However, they faced domain adaptation, small objects retention, and feature extraction issues of complex microscopic images. In this study, we introduced multi-scale feature fusion-based domain adaptive model for breast cancer classification using histopathology images. It has two blocks and six lightweight submodels where each block contains three models. Dilated layers are used in sub-models to overcome the disappearing of small objects in deep layers. Reducing the disappearing of small objects helped to extract better features for higher performance. Multiple heterogeneous feature extractors are used in this study which helped to extract various features. Extracted features are fused and reduced by retaining better features. Learning of model from natural images to complex microscopic images has limitation of domain adaptation. Same domain transfer learning is used in this study to overcome the limitations of different domain transfer learning. Model is trained on patchcamelyon17 dataset and weights of this training are further used for same domain transfer learning. Pre-trained weights are further used for the training of proposed model on BreaKHis dataset. A number of conventional data augmentation techniques are used as complex models require higher number of samples for the tuning of weights. Local window based CLAHE contrast enhancement technique is used to increase foreground-background contrast and remove noise. The proposed model achieved 98.00% precision, 98.15% recall, 98.08% f-measure, and 98.23% accuracy on test data. To best of our knowledge, it surpassed state-of-the-art models.

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“…There are a number of studies in the literature which have been applied to these datasets. For instance, many researchers have utilised BreakHis dataset to test their networks [11,12,19,20,[26][27][28][45][46][47][48][49][50][51][52]. Zhou et al [45] proposed a novel resolution adaptive network (RAN) to classify different forms of breast cancer.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, many researchers have utilised BreakHis dataset to test their networks [11,12,19,20,[26][27][28][45][46][47][48][49][50][51][52]. Zhou et al [45] proposed a novel resolution adaptive network (RAN) to classify different forms of breast cancer. On the BreakHis dataset, it produces a classification accuracy of 98.17%.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Breast Cancer Histopathological Image Classification using Attention-Based High Order Covariance Pooling

Waqas,

Ahmed,

Maul

et al. 2023

Preprint

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The type of cancer that affects female patients most frequently is breast cancer. Computer-aided diagnosis, these days, proves to be helpful for many diseases including breast cancer. Deep learning based approaches have yielded encouraging results in many computer vision applications. In the case of breast cancer histopathological image classification, many methods have been proposed but the accuracy is still insufficient for full deployment. Due to this reason, we propose a deep learning based approach, DHA-Net which consists of an attention module and a high-order pooling layer incorporated into a ResNet-18 architecture. DHA-Net was applied to three well-known datasets in the field of breast cancer histopathological image classification, i.e. BreakHis, BACH2018 and a closely related Kaggle-Breast cancer histopathology dataset. In this area of medical imaging, experiments have shown that DHA-Net works better than other existing techniques.

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Breast Cancer Classification From Histopathological Images Using Resolution Adaptive Network

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 26 publications

References 45 publications

GARL-Net: Graph Based Adaptive Regularized Learning Deep Network for Breast Cancer Classification

GARL-Net: Graph Based Adaptive Regularized Learning Deep Network for Breast Cancer Classification

MSF-Model: Multi-Scale Feature Fusion-Based Domain Adaptive Model for Breast Cancer Classification of Histopathology Images

Enhancing Breast Cancer Histopathological Image Classification using Attention-Based High Order Covariance Pooling

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