现有的深度学习方法在癌症的识别中仅利用深层特征,忽略了浅层网络输出保存的空域细节信息,从而导致识别精度不理想。为了进一步推进临床应用,协助医生提高乳腺癌病理诊断的一致性和效率,提出一种基于改进Inception-v3的图像分类优化算法,该算法通过模型改进、迁移学习对网络模型进行优化。对大型公开数据库病理学图像进行乳腺癌分类,对所提算法所改进的模型与现有的基于深度学习的图像分类模型进行了比较。实验结果表明,所提算法所改进的模型不仅优于传统深度学习方法,准确率达到96%,有效地提高了深度学习模型对于乳腺癌诊断的性能,并且为进一步推进临床应用奠定理论和实践基础。

Existing deep learning methods only use deep layer features for recognizing cancer and ignore the spatial information stored in the output of the surface network, yielding unsatisfactory recognition accuracy. To further promote clinical applications and aid doctors improve the consistency and efficiency of breast cancer pathological diagnosis, an improved Inception-v3 image classification optimization algorithm is proposed. This algorithm optimizes the network model through model improvement and transfer learning. Breast cancer was classified based on the pathological images of a large open database. The improved model of the proposed algorithm is superior to the traditional deep learning method, with an accuracy rate of 96%, which effectively improves the performance of the deep learning model for breast cancer diagnosis. Moreover, the proposed algorithm lays a theoretical and practical foundation for further clinical applications.