Author(s)

Eri Matsuyama^1*, Megumi Takehara², Du-Yih Tsai³

¹Department of Radiological Sciences, International University of Health and Welfare, Tochigi, Japan.
²Breast Center, Dokkyo Medical University Hospital, Tochigi, Japan.
³Department of Biomedical Engineering, Hungkuang University, Taichung City, Taiwan, China.

Classification of breast density is significantly important during the process of breast diagnosis. The purpose of this study was to develop a useful computer-ized tool to help radiologists determine the patient’s breast density category on the mammogram. In this article, we presented a model for automatically classi-fying breast densities by employing a wavelet transform-based and fine-tuned convolutional neural network (CNN). We modified a pre-trained AlexNet model by removing the last two fully connected (FC) layers and appending two newly created layers to the remaining structure. Unlike the common CNN-based methods that use original or pre-processed images as inputs, we adopted the use of redundant wavelet coefficients at level 1 as inputs to the CNN model. Our study mainly focused on discriminating between scattered density and heterogeneously dense which are the two most difficult density cat-egories to differentiate for radiologists. The proposed system achieved 88.3% overall accuracy. In order to demonstrate the effectiveness and usefulness of the proposed method, the results obtained from a conventional fine-tuning CNN model was compared with that from the proposed method. The results demon-strate that the proposed technique is very promising to help radiologists and serve as a second eye for them to classify breast density categories in breast cancer screening.

Mammography, Mammary Gland Density, CNN, Wavelet Transform

Matsuyama, E. , Takehara, M. and Tsai, D. (2020) Using a Wavelet-Based and Fine-Tuned Convolutional Neural Network for Classification of Breast Density in Mammographic Images. Open Journal of Medical Imaging, 10, 17-29. doi: 10.4236/ojmi.2020.101002.