Computer Assisted Diagnosis of Breast Cancer Using Histopathology Images and Convolutional Neural Networks

Vikranth, Chinnapapakkagari Sreenivasa; Jagadeesh, Bodavula; K, Rakesh; Mohammad, Doriginti; Krishna, Sruthi; S, Remya Ajai A

doi:10.1109/aisp53593.2022.9760669

Cited by 21 publications

(5 citation statements)

References 18 publications

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“…Recently proposed models, including a MobileNetV2-based classifier that demonstrated 91-92% accuracy in the classification of breast cancer images and an Inception-V3-based model that achieved AUC values of more than 0.889 in the classification of rhabdomyosarcoma have adopted softmax activation in the final layer. [33,34] While this may be relevant when the classes are mutually exclusive, the presence of infiltration implies that tissue images can contain both benign and malignant cells. Multiple classes may therefore coexist within the images.…”

Section: Discussionmentioning

confidence: 99%

Detecting Tumor Infiltration in Diffuse Gliomas with Deep Learning

Prathaban,

Wu,

Tan

et al. 2023

Advanced Intelligent Systems

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Glioblastoma tumor recurrences often occur in brain tissue areas harboring infiltrating tumor cells, resembling healthy tissue in brain imaging. Demarcating infiltrative regions for aggressive resections is critical for improving prognostic outcomes but is challenging in neurosurgery. Herein, a multilayer sigmoid‐activated convolutional neural network (MLS‐CNN) is developed for rapidly distinguishing glioma tumor infiltration in brain tissue histology. Unlike conventional multiclass classifiers, the MLS‐CNN employs sigmoidal activation to accommodate coexisting classes within patch images. 59 811 image patches (25 807 infiltrating edge, 15 178 normal brain, 18 826 cellular tumor) from 73 brain tissue samples are extracted to train the classifier. The model achieves an accuracy of 91.70% (sensitivity: 91.62%; specificity: 91.78%) and area under the curve (AUC) of 0.964 in distinguishing infiltrating edges, outperforming the current state‐of‐the‐art Vision Transformer (ViT) (accuracy: 89.45; AUC: 0.947). The MLS‐CNN is computationally efficient, generating predictions within 11.5 s in comparison to 81.4 s for ViT. The predictions strongly correlate with In Situ Hybridization expression intensities, validating the utility of the MLS‐CNN model in spatial genomics investigations in gliomas. The robust model can therefore serve as an automatic and accurate classifier to help pathologists identify infiltrative glioma for better diagnosis and patient outcomes in brain oncology.

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Section: Discussionmentioning

confidence: 99%

Detecting Tumor Infiltration in Diffuse Gliomas with Deep Learning

Prathaban,

Wu,

Tan

et al. 2023

Advanced Intelligent Systems

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“…Various flavors of neural networks and conventional machine learning techniques are exploited for the recognition of malignancy from breast histopathology images. 28 , 29 , 30 , 31 , 32…”

Section: Related Workmentioning

confidence: 99%

An interpretable decision-support model for breast cancer diagnosis using histopathology images

Krishna

Suganthi²,

Bhavsar

et al. 2023

Journal of Pathology Informatics

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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%

“…First of all, a number of researchers use Convolutional Neural Networks (CNNs) for feature extraction followed by any classifier to classify the features obtained from the CNN. The research paper [20] proposed a computer-aided-Diagnostic (CAD) system for breast cancer histopathological images. It consists of a CNN for obtaining features and a support vector machine (SVM) for classification.…”

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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Computer Assisted Diagnosis of Breast Cancer Using Histopathology Images and Convolutional Neural Networks

Cited by 21 publications

References 18 publications

Detecting Tumor Infiltration in Diffuse Gliomas with Deep Learning

Detecting Tumor Infiltration in Diffuse Gliomas with Deep Learning

An interpretable decision-support model for breast cancer diagnosis using histopathology images

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

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