Feb 22, 2018 · In this paper, we propose a deep learning-based method for classification of H&E stained breast tissue images released for BACH challenge 2018.
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We propose a method named DenTnet to classify breast cancer histopathological images chiefly. DenTnet utilizes the principle of transfer learning.
Oct 19, 2022 · Image classification methods based on deep learning frameworks, such as the convolutional neural network (CNN) and recurrent neural network (RNN) ...
Oct 9, 2020 · This paper has a two-fold purpose. The first aim is to investigate the various deep learning models in classifying breast cancer histopathology images.
Jun 6, 2018 · In this paper, we propose a deep learning-based method for classification of H&E stained breast tissue images released for BACH challenge
Breast Cancer Classification from Histopathological Images with ...
pmc.ncbi.nlm.nih.gov › PMC6646497
The images were classified according to four different classes: normal tissue, benign lesion, in situ carcinoma, and invasive carcinoma. Images were also ...
Sep 16, 2022 · This paper presents a deep learning approach to automatically classify hematoxylin-eosin-stained breast cancer microscopy images into normal tissue, benign ...
A deep learning-based method for classification of H&E stained breast tissue images released for BACH challenge 2018 by fine-tuning Inception-v3 ...
We developed and utilized ensemble deep learning algorithms for addressing the tasks of classifying (1) breast cancer subtype and (2) breast cancer ...
This study proposes a transfer learning-based artificially intelligent (AI) system to classify breast cancer from the histopathological images of the breast.