Research and Implementation of Breast Cancer Intelligent Recognition Algorithm Based on Deep Convolutional Neural Network

Breast cancer is one of the most dangerous cancers with a particularly high mortality rate for women. In the face of thousands of female pathological pictures, how to use computer related control algorithms to quickly and efficiently intelligently idently female breast cancer images has become an ur...

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Bibliographic Details
Published in:Journal of physics. Conference series Vol. 1634; no. 1; pp. 12176 - 12181
Main Authors: Chen, Taixing, Wang, Lidan, Li, Yunfei, Duan, Shukai
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01.09.2020
Subjects:
Algorithms
Artificial neural networks
Breast cancer
Computer control
Computer vision
Control algorithms
Control theory
Image classification
Medical imaging
Network computers
Neural networks
Object recognition
Parameters
Physics
ISSN:1742-6588, 1742-6596
Online Access:Get full text
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Summary:Breast cancer is one of the most dangerous cancers with a particularly high mortality rate for women. In the face of thousands of female pathological pictures, how to use computer related control algorithms to quickly and efficiently intelligently idently female breast cancer images has become an urgent problem in the world. With the development of computer vision and deep convolutional neural networks, this paper applies the convolutional neural network (CNN) algorithm to solve the classification and recognition computer tasks of histological images for breast cancer, aiming to learn from tens of thousands of histological images features for breast cancer, and to assist doctors in diagnosing patients' disease was found by computer control algorithm. There are many well-known neural network computer algorithms, such as AlexNet, Inception-Net, ResNet. Image recognition for breast is a binary classification problem, and it is not appropriate to use a ResNet network with a large amount of parameters. Therefore, this paper borrowed Alexnet algorithm and redesigned the algorithm, then used it for breast cancer experiments. We compared with the capsule algorithm used by Anupama authors. Our experiment improves the accuracy rate by 2%, and reduces the amount of neural network parameters, which greatly speeds up the training time.
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1634/1/012176