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Utilization of DenseNet201 for diagnosis of breast abnormality

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Abstract

As one of the leading killers of females, breast cancer has become one of the heated research topics in the community of clinical medical science and computer science. In the clinic, mammography is a publicly accepted technique to detect early abnormalities such as masses and distortions in breast leading to cancer. Interpreting the images, however, is time-consuming and error-prone for radiologists considering artificial factors including potential fatigue. To improve radiologists’ working efficiency, we developed a semi-automatic computer-aided diagnosis system to classify mammograms into normality and abnormality and thus to ease the process of making a diagnosis of breast cancer. Through transferring deep convolutional neural network DenseNet201 on the basis of suspicious regions provided by radiologists into our system, we obtained the network we termed DenseNet201-C, which achieved a high diagnostic accuracy of 92.73%. The comparison results between our method and the other five methods show that our method achieved the highest accuracy.

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Acknowledgements

This paper is financially supported by Henan Key Research and Development Project (182102310629), Guangxi Key Laboratory of Trusted Software (kx201901), Open Fund of Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology (17-259-05-011K), National key research and development plan (2017YFB1103202), and Natural Science Foundation of China (61602250, U1711263, U1811264). Also, Xiang Yu holds a China Scholarship Council studentship with the University of Leicester.

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Authors and Affiliations

  1. Department of Informatics, University of Leicester, Leicester, LE1 7RH, UK

    Xiang Yu & Yu-Dong Zhang

  2. School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454000, Henan, People’s Republic of China

    Yu-Dong Zhang

  3. Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, 361005, People’s Republic of China

    Nianyin Zeng

  4. College of Information Science and Engineering, Hunan Normal University, Changsha, 410000, People’s Republic of China

    Shuai Liu

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  1. Xiang Yu
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  2. Nianyin Zeng
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  4. Yu-Dong Zhang
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Correspondence to Nianyin Zeng, Shuai Liu or Yu-Dong Zhang.

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Yu, X., Zeng, N., Liu, S. et al. Utilization of DenseNet201 for diagnosis of breast abnormality. Machine Vision and Applications 30, 1135–1144 (2019). https://doi.org/10.1007/s00138-019-01042-8

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