For the best breast cancer prediction, a new method called a "Hybrid CNN" combining the Sine Cosine Algorithm (SCA) with a transfer learning algorithm has been presented. Mini-batch size and drop-out rate are just two of the factors that the SCA algorithm may fine-tune. To stop the ...
Testing the model on the breast cancer histology (BACH) dataset33 and Yan’s dataset30, MSMV-PFENet can achieve a good performance in terms of accuracy, precision, recall, and F1 score. Methods Figure 1 Diagram of breast cancer pathological image classification using MSMV-PFENet. (a) KREM...
Detection of Breast Cancer Using Histopathological Image Classification Dataset with Deep Learning Techniques Cancer is one of the top causes of mortality, and it arises when cells in the body grow abnormally, like in the case of breast cancer. For people all aroun... V Reshma,N Arya,SS Ahma...
In this study, we propose a breast cancer multi-classification method using a newly proposed deep learning model. The structured deep learning model has achieved remarkable performance (average 93.2% accuracy) on a large-scale dataset, which demonstrates the strength of our method in providing an ...
Grading Image dataset 1. Introduction Cancer is a serious public health issue worldwide and the second leading cause of death in the United States [1]. According to the International Agency for Research on Cancer (IARC), about 18.1 million new cases and 9.6 million deaths caused by cancer wer...
A Dataset for Breast Cancer Histopathological Image Classification IEEE Transactions on Biomedical Engineering (TBME), 63 (7) (2016), pp. 1455-1462 View in ScopusGoogle Scholar 20 Karen Simonyan, Andrew Zisserman, “Very Deep Convolutional Networks for Large-Scale Imag...
breastcancer-wisconsin.data2021-02-2719.42KB breastcancer-wisconsin.names2021-02-275.52KB 文档 Breast Cancer Dataset乳腺癌数据集 1.Overview Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the...
Breast cancer is a common malignancy and a leading cause of cancer-related deaths in women worldwide. Its early diagnosis can significantly reduce the morbidity and mortality rates in women. To this end, histopathological diagnosis is usually followed as
Successful human-AI collaboration could greatly contribute to breast cancer mammographic screening. Here, the authors use a large-scale retrospective mammography dataset to simulate and compare five plausible AI-integrated screening pathways, finding optimal ways in which human-AI collaboration could be imp...
page_id=1616. Our dataset is not only the largest publicly released dataset for breast cancer histopathological image classification, but it covers as many different subclasses spanning different age groups as possible, thus providing enough data diversity to alleviate the problem of relatively low ...