基于并行卷积神经网络的X线图像成像部位识别

Using Parallel Convolutional Neural Networks for Treatment Position Recognition in X-ray Images

医学图像中成像部位的识别是医学图像处理的关键技术。鉴于卷积神经网络优异的特征提取和分类识别能力,该文提出一种并行卷积神经网络结构,通过使用不同尺寸的卷积核,提取X线图像中不同尺寸大小的局部特征,实现图像中成像部位识别。实验分析可知,并行卷积神经网络能够提取更多维度和有代表性的图像特征,具有较强的医学图像中成像部位分类识别能力。

Treatment position recognition in medical images is a key technique in medical image processing.Due to the excellent performance of convolutional neural networks on features extraction and classification,an architecture of parallel convolutional neural networks is proposed to recognize treatment positions in X-ray images,which uses convolution kernels of different sizes to extract local features of different sizes in these images.The experimental analysis shows that parallel convolution neural networks,which can extract representative image features with more dimensions,are competent to classify and recognize treatment positions in medical images.