Abstract
Breast cancer is the second leading cause of death in women worldwide and of the methods for the detection of breast cancer, Mammography is considered promising and effective. In order to improve the detection, the study explored automatic recognition of Mammary X-ray Molybdenum target images on the basis of the clustered distribution pleomorphic calcification images of Breast Imaging Reporting & Data System category 4 (BI-RADS 4) obtained from an open access database – Digital Database for Screening Mammography (DDSM). The region of interest (ROI) of molybdenum target images was firstly segmented into sub-images by coordinate matching technology, and then the sub-images were scanned row by row and subdivided into mini-images. Those mini-images containing lesions were thus screened out and used as the objects of neural network recognition. Pattern recognition was carried out via the classical convolutional neural networks such as VGGNet16, VGGNet11 and AlexNet, and the improved AlexNet network without LRN layer. The results showed that identification and subdivision of the ROIs together with the improved AlexNet network could significantly improve the performance of recognition. By comparison with other methods, the new methods developed herein could provide additional and useful information for clinical diagnosis, and lay a technical foundation for refining classification of BI-RADS4 images into sub-categories and furthering accurate diagnosis.
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Acknowledgements
The authors express their thanks to Zhou Zhijun, Pan Yonghao, Liu Ruochen and Guo Zongan for their participation of the research and providing part of technical supports. The authors also acknowledge the funding supports for this research from National Key Research and Development Programme of China (Grant No. 2016YFC1303003).
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Zhang, M., Liu, W., Zhang, X., Chen, Y., Gu, Y., Xiao, Q. (2020). Application of Image Segmentation and Convolutional Neural Network in Classification Algorithms for Mammary X-ray Molybdenum Target Image. In: Su, R., Liu, H. (eds) Medical Imaging and Computer-Aided Diagnosis. MICAD 2020. Lecture Notes in Electrical Engineering, vol 633. Springer, Singapore. https://doi.org/10.1007/978-981-15-5199-4_18
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