DAN : Breast Cancer Classification from High-Resolution Histology Images Using Deep Attention Network

Sanyal, Ritabrata; Jethanandani, Manan; Sarkar, Ram

doi:10.1007/978-981-15-6067-5_35

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…For the classification of IDC tumors, their study reported an accuracy of 89% without performing data augmentation. The attention-based deep CNN model for BC detection is proposed by Sanyal et al [32]. In their study different image patches that contained higher information gained higher attention for further processing of detecting cancer for this purpose histopathology images with high resolution was utilized.…”

Section: Related Workmentioning

confidence: 99%

A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

Umer¹,

Sharif²,

Alhaisoni³

et al. 2023

Computer Systems Science and Engineering

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Breast cancer (BC) is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year. The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6% of total cases. Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths. The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis. Manual diagnosis of BC is a complex and challenging task. This work proposed a deep learning-based (DL) solution for the early detection of this deadly disease from histopathology images. To evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is utilized. The proposed automatic diagnosis of BC detection and classification mainly involves three steps. Initially, a DL model is proposed for feature extraction. Secondly, the extracted feature vector (FV) is passed to the proposed novel feature selection (FS) framework for the best FS. Finally, for the classification of BC into invasive ductal carcinoma (IDC) and normal class different machine learning (ML) algorithms are used. Experimental outcomes of the proposed methodology achieved the highest accuracy of 92.7% which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.

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Section: Related Workmentioning

confidence: 99%

A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

Umer¹,

Sharif²,

Alhaisoni³

et al. 2023

Computer Systems Science and Engineering

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“…Instead of giving equal significance to all the patches, the authors in [ 22 ] presented an attention mechanism that allowed the network to focus on the relevant features of patches. A weighted representation of all the constituent patches of an image was used for learning.…”

Section: Literature Surveymentioning

confidence: 99%

Computer Aided Breast Cancer Detection Using Ensembling of Texture and Statistical Image Features

Roy

Das

Kar

et al. 2021

Sensors

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Breast cancer, like most forms of cancer, is a fatal disease that claims more than half a million lives every year. In 2020, breast cancer overtook lung cancer as the most commonly diagnosed form of cancer. Though extremely deadly, the survival rate and longevity increase substantially with early detection and diagnosis. The treatment protocol also varies with the stage of breast cancer. Diagnosis is typically done using histopathological slides from which it is possible to determine whether the tissue is in the Ductal Carcinoma In Situ (DCIS) stage, in which the cancerous cells have not spread into the encompassing breast tissue, or in the Invasive Ductal Carcinoma (IDC) stage, wherein the cells have penetrated into the neighboring tissues. IDC detection is extremely time-consuming and challenging for physicians. Hence, this can be modeled as an image classification task where pattern recognition and machine learning can be used to aid doctors and medical practitioners in making such crucial decisions. In the present paper, we use an IDC Breast Cancer dataset that contains 277,524 images (with 78,786 IDC positive images and 198,738 IDC negative images) to classify the images into IDC(+) and IDC(-). To that end, we use feature extractors, including textural features, such as SIFT, SURF and ORB, and statistical features, such as Haralick texture features. These features are then combined to yield a dataset of 782 features. These features are ensembled by stacking using various Machine Learning classifiers, such as Random Forest, Extra Trees, XGBoost, AdaBoost, CatBoost and Multi Layer Perceptron followed by feature selection using Pearson Correlation Coefficient to yield a dataset with four features that are then used for classification. From our experimental results, we found that CatBoost yielded the highest accuracy (92.55%), which is at par with other state-of-the-art results—most of which employ Deep Learning architectures. The source code is available in the GitHub repository.

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“…Researchers have shown the immense potential of DL-based applications for mammogram image processing in terms of providing reliable breast cancer predictions [ 4 , 33 – 35 ]. Moreover, the attention mechanism exploits the most important regions of an image by paying more attention to the same [ 36 , 37 ]. Furthermore, FS approaches reduce the number of features, whereas local search helps to increase the exploitation capability of the FS method and produces the most optimal subset of features [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Deep feature selection using local search embedded social ski-driver optimization algorithm for breast cancer detection in mammograms

Pramanik

Mukhopadhyay²,

Mirjalili³

et al. 2022

Neural Comput & Applic

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Breast cancer has become a common malignancy in women. However, early detection and identification of this disease can save many lives. As computer-aided detection helps radiologists in detecting abnormalities efficiently, researchers across the world are striving to develop reliable models to deal with. One of the common approaches to identifying breast cancer is through breast mammograms. However, the identification of malignant breasts from mass lesions is a challenging research problem. In the current work, we propose a method for the classification of breast mass using mammograms which consists of two main stages. At first, we extract deep features from the input mammograms using the well-known VGG16 model while incorporating an attention mechanism into this model. Next, we apply a meta-heuristic called Social Ski-Driver (SSD) algorithm embedded with Adaptive Beta Hill Climbing based local search to obtain an optimal features subset. The optimal features subset is fed to the K-nearest neighbors (KNN) classifier for the classification. The proposed model is demonstrated to be very useful for identifying and differentiating malignant and healthy breasts successfully. For experimentation, we evaluate our model on the digital database for screening mammography (DDSM) database and achieve 96.07% accuracy using only 25% of features extracted by the attention-aided VGG16 model. The Python code of our research work is publicly available at: https://github.com/Ppayel/BreastLocalSearchSSD.

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DAN : Breast Cancer Classification from High-Resolution Histology Images Using Deep Attention Network

Cited by 5 publications

References 9 publications

A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

Computer Aided Breast Cancer Detection Using Ensembling of Texture and Statistical Image Features

Deep feature selection using local search embedded social ski-driver optimization algorithm for breast cancer detection in mammograms

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