The comparative study of deep learning neural network approaches for breast cancer diagnosis
Breast cancer is one of the life-threatening cancer that leads to the most death due to cancer among women. Early diagnosis might help to reduce mortality. Thus, this research aims to study different approaches to the deep learning neural network model for breast cancer early detection for better...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
International Association Of Online Engineering
2023
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| Online Access: | http://eprints.utem.edu.my/id/eprint/27492/2/0260402082023281.PDF http://eprints.utem.edu.my/id/eprint/27492/ https://online-journals.org/index.php/i-joe/article/view/34905 https://doi.org/10.3991/ijoe.v19i06.34905 |
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| Summary: | Breast cancer is one of the life-threatening cancer that leads
to the most death due to cancer among women. Early diagnosis might help to reduce mortality. Thus, this research aims to study different approaches to the deep learning neural network model for breast cancer early detection for better prognosis. The performance of deep learning approaches such as Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Convolution Neural
Network (CNN) is evaluated using the dataset from the University of Wisconsin. The findings show ANN achieved high accuracy of 99.9 % compared to others in detecting breast cancer. ANN can deliver better results with the provided dataset. However, more improvement is needed for better performance to ensure that the approach used is reliable enough for early breast cancer diagnosis. |
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