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KMID : 0357520190420010009
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Journal of Radiological Science and Technology
2019 Volume.42 No. 1 p.9 ~ p.17
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A Feasibility Study on the Improvement of Diagnostic Accuracy for Energy-selective Digital Mammography using Machine Learning
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Eom Ji-Soo
Lee Seung-Wan
Kim Burn-Young
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Abstract
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Although digital mammography is a representative method for breast cancer detection. It has a limitation in detecting and classifying breast tumor due to superimposed structures. Machine learning, which is a part of artificial intelligence fields, is a method for analysing a large amount of data using complex algorithms, recognizing patterns and making prediction. In this study, we proposed a technique to improve the diagnostic accuracy of energy-selective mammography by training data using the machine learning algorithm and using dual-energy measurements. A dual-energy images obtained from a photon-counting detector were used for the input data of machine learning algorithms, and we analyzed the accuracy of predicted tumor thickness for verifying the machine learning algorithms. The results showed that the classification accuracy of tumor thickness was above 95% and was improved with an increase of imput data. Therefore, we expect that the diagnostic accuracy of energy-selective mammography can be improved by using machine learning.
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KEYWORD
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Machine learning, Digital mammography, Dual-energy, Classification, Tumor thickness
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