A fully automatic microcalcification detection approach based on deep convolution neural network

Guanxiong Cai; Yanhui Guo; Yaqin Zhang; Genggeng Qin; Yuanpin Zhou; Yao Lu

doi:10.1117/12.2293593

27 February 2018 A fully automatic microcalcification detection approach based on deep convolution neural network

Guanxiong Cai, Yanhui Guo, Yaqin Zhang, Genggeng Qin, Yuanpin Zhou, Yao Lu

Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 105752Q (2018) https://doi.org/10.1117/12.2293593
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Breast cancer is one of the most common cancers and has high morbidity and mortality worldwide, posing a serious threat to the health of human beings. The emergence of microcalcifications (MCs) is an important signal of early breast cancer. However, it is still challenging and time consuming for radiologists to identify some tiny and subtle individual MCs in mammograms. This study proposed a novel computer-aided MC detection algorithm on the full field digital mammograms (FFDMs) using deep convolution neural network (DCNN). Firstly, a MC candidate detection system was used to obtain potential MC candidates. Then a DCNN was trained using a novel adaptive learning strategy, neutrosophic reinforcement sample learning (NRSL) strategy to speed up the learning process. The trained DCNN served to recognize true MCs. After been classified by DCNN, a density-based regional clustering method was imposed to form MC clusters. The accuracy of the DCNN with our proposed NRSL strategy converges faster and goes higher than the traditional DCNN at same epochs, and the obtained an accuracy of 99.87% on training set, 95.12% on validation set, and 93.68% on testing set at epoch 40. For cluster-based MC cluster detection evaluation, a sensitivity of 90% was achieved at 0.13 false positives (FPs) per image. The obtained results demonstrate that the designed DCNN plays a significant role in the MC detection after being prior trained.

Citation Download Citation

Guanxiong Cai, Yanhui Guo, Yaqin Zhang, Genggeng Qin, Yuanpin Zhou, and Yao Lu "A fully automatic microcalcification detection approach based on deep convolution neural network", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105752Q (27 February 2018); https://doi.org/10.1117/12.2293593

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available