Preliminary Study of Using Neural Network on Breast Mass Palpation
碩士 === 國立臺北科技大學 === 自動化科技研究所 === 93 === Palpation diagnosis is a convenient and time-saving way among several diagnosis methods for breast cancer detection. However, self breast examination does not raise much significance for women in Taiwan. In this study, we calculated the detected forces as feat...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2005
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| Online Access: | http://ndltd.ncl.edu.tw/handle/xq23n8 |
| Summary: | 碩士 === 國立臺北科技大學 === 自動化科技研究所 === 93 === Palpation diagnosis is a convenient and time-saving way among several diagnosis methods for breast cancer detection. However, self breast examination does not raise much significance for women in Taiwan. In this study, we calculated the detected forces as feature parameters in the lateral in associated with normal directions in the process of longitudinal test and normal test of tumors by curve fitting. And the derived parameters are thus fed to the neural network as input vectors so as to computerize the size and depth of tumors. In this study, we applied 61 samples during training process and 12 samples during the testing process in our neural network. The experimental results depict that the both of the accuracy is 72% in tumor size and depth during training process. And we could observed that the accuracy is more than 50% from the tumor size and depth during testing process. These results show that utilizing the neural network for recognizing size and depth of tumors is potentially detectable. And the accuracy of identifying various training and testing samples could be increased if the time needed for convergence during the training process could be decreased.
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