Використання згорткових нейронних мереж для діагностування раку молочної залози
During the last few years, Convolutional Neural Networks (CNN) have been widely used in Computer-Aided Detection and the medical image analysis. The main idea of this paper is to modify CNN’s architectures to achieve the better sensitivity and the precision for detecting breast cancer at an early st...
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| Date: | 2019 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
| Published: |
The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"
2019
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| Subjects: | |
| Online Access: | http://journal.iasa.kpi.ua/article/view/183548 |
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| Journal Title: | System research and information technologies |
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System research and information technologies| Summary: | During the last few years, Convolutional Neural Networks (CNN) have been widely used in Computer-Aided Detection and the medical image analysis. The main idea of this paper is to modify CNN’s architectures to achieve the better sensitivity and the precision for detecting breast cancer at an early stage compared to existing methods. For this purpose, several factors were considered before CNN training such as the data processing, model, dataset, etc. In the proposed model the following hyperparameters were the following: the dropout rate 0,2, epoch 38 and batch size 33. Besides the hyperparameters, two fully connected layers in the modified model were used. An average recall (sensitivity) in the recent works was 74%. The precision and recall of proposed model for breast cancer classification were 66,66% and 85,7%, respectively. |
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