| Title |
Comparison of deep convolutional neural networks for classification of breast ultrasound images |
| Authors |
박주영(Juyoung Park) ; 김이삭(Yisak Kim) ; 유창완(Chang-Wan Ryu) ; 김형석(Hyungsuk Kim) |
| DOI |
https://doi.org/10.5370/KIEE.2020.71.1.176 |
| Keywords |
Breast Ultrasound; Breast Cancer; Tumor; Classification; VGG; ResNet; InceptionNet; DenseNet; EfficientNet; Convolutional Neural Network |
| Abstract |
Breast ultrasound has been widely utilized for classifying tumors into benignancy and malignancy. The limitations of traditional breast ultrasound are the handcrafted features obtained by well-trained sonographers and subjective decision according to different individual experiences. Recently, CNN-based deep learning techniques have exhibited better performance in medical images. However, most research for deep learning in medical ultrasound adopts CNN models developed for natural images due to the lack of common standard and dataset. In this paper, we compare six DCNN models which exhibit good performance for natural images - VGGNet, ResNet, InceptionNet, DenseNet, and EfficientNet. Our classification results demonstrate that CNN models of relatively lower performance on natural images show better performance on gray-scale ultrasound images and further study of CNN models are needed focusing on the features of medical images. |