论文标题
基于现代乳房X线摄影，使用混合动力转移学习诊断乳腺癌
Diagnosis of Breast Cancer Based on Modern Mammography using Hybrid Transfer Learning
论文作者
论文摘要
乳腺癌是女性常见的癌症。乳腺癌的早期发现可以大大提高女性的存活率。本文主要集中于转移学习过程以检测乳腺癌。在本文中提出并实施了修改后的VGG（MVGG），剩余网络，移动网络。 DDSM数据集在本文中使用。实验结果表明，我们提出的混合动力传输学习模型（MVGG16和ImageNet的融合）提供了88.3％的精度，而时代数为15。另一方面，只有修改的VGG 16体系结构（MVGG 16）提供准确的80.8％，而Mobilenet的精度可提供77.2％的精度。因此，可以清楚地说明，与单个体系结构相比，提出的混合预培训网络的表现很好。该体系结构可以被视为放射科医生的有效工具，以降低假阴性和假阳性率。因此，将提高乳房X线摄影分析的效率。
Breast cancer is a common cancer for women. Early detection of breast cancer can considerably increase the survival rate of women. This paper mainly focuses on transfer learning process to detect breast cancer. Modified VGG (MVGG), residual network, mobile network is proposed and implemented in this paper. DDSM dataset is used in this paper. Experimental results show that our proposed hybrid transfers learning model (Fusion of MVGG16 and ImageNet) provides an accuracy of 88.3% where the number of epoch is 15. On the other hand, only modified VGG 16 architecture (MVGG 16) provides an accuracy 80.8% and MobileNet provides an accuracy of 77.2%. So, it is clearly stated that the proposed hybrid pre-trained network outperforms well compared to single architecture. This architecture can be considered as an effective tool for the radiologists in order to reduce the false negative and false positive rate. Therefore, the efficiency of mammography analysis will be improved.
