深度学习在医学图像识别中的应用研究

Application and Research of Deep Learning in Medical Image Recognition

对宫颈的检查图像进行识别可以有效预防宫颈癌的发生,然而,正确分辨出患癌趋势的图像对人类来说是极难掌握的技术。使用深度学习方法对宫颈检查图像进行识别分类,以辅助人类专家做出诊断;首先对图像进行手动裁剪来增大信噪比,把原始图像进行格式转换来提高数据读取效率,并在图像输入模型之前进行随机变换以增大训练集;然后建立CNN模型,进行训练并调整参数;最后在测试集上分类准确率达到了89.1%,结果表明,使用深度学习辅助专家进行宫颈癌的早期诊断是可行的。

Recognizing cervical medical image was useful to prevent cervical carcinoma. However, correctdistinguish different cervical medical image was difficult. Recognizing cervical medical image with deep learning method was studied. Firstly, images were cut manually for enlarging Signal-to-noise ratio andconverting format for increasing data reading efficiency, besides transforming images randomly for ging training set. Then a CNN model was buitt for training and adjusting parameters. Finally it obtained89.1% classification accuracy on test set, and it was illustrated that it is feasible to use deep learning method as auxiliary.