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About A Survey For Breast Histopathology Image Analysis Using Classical and Deep Neural Networks
This document summarizes over 60 works related to using classical and deep neural networks for breast histopathology image analysis (BHIA). It finds that classical neural networks were commonly used for BHIA tasks like classification and segmentation from the 2000s to early 2010s due to hardware limitations. Since 2012, deep neural networks started to be applied to BHIA tasks like mitosis detection, achieving F1 scores up to 78.2%. In 2016, the BreaKHis dataset was released, spurring more works using deep learning models like LeNet and AlexNet for BHIA classification tasks. The document provides an overview of the development and effectiveness of neural networks for automated analysis in breast cancer diagnosis.
Detailed Information
| Author: | ['Nadeem Shoukat'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 12 |
| Language: | English |
| Format: | |
| Price: | FREE |
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