For this purpose, we apply deep learning by using convolutional neural networks (CNN) to classify abnormalities, benign or malignant, in mammographic images ...
This research addresses the crucial need to gain insights into the decision-making process of convolutional neural networks for mammogram classification, ...
Deep learning and image processing are used to classify and segment breast tumor images, specifically in ultrasound (US) modalities, to support clinical ...
The model has achieved classification accuracy of 94.31% for cancer diagnosis and 95.01% for abnormality detection. In [85] , four CNN-based models have ...
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The rest of this paper is as follows: Section 2 outlines the related work of the benign and malignant classification of mammogram images based on CNN. The ...
A multi-view feature fusion network model for classification of mammograms from two views is constructed and a multi-scale attention DenseNet is proposed as ...
The Breast Cancer Histopathological Image Classification (BreakHis) consists of 5429 malignant samples and 2480 benign samples. So, there are 9109 samples ...
Therefore, we proposed a deep neural network model to classify benign and malignant tumors in digital mammograms. Our model is an improved version of the ...
Jan 30, 2023 · The proposed hybrid CNN-LSTM model showed the highest overall accuracy of 99% for binary classification of benign and malignant cancer.
This paper proposes a new CAD technique, which relies on customized deep convolutional neural network to detect and classify breast cancer into malignant and ...