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A Classification Method of Breast Pathological Image Based on Residual Learning

机译：基于剩余学习的乳房病理形象分类方法

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Today, breast cancer has become a globally recognized tumor disease with high incidence and high mortality. The use of advanced convolutional neural network for the auxiliary diagnosis of breast pathological images is the general trend. Due to the complex background of the medical pathology image and more noise, a bilateral filter is used for noise reduction. Aiming at the problem of refinement, complexity and weakening of the morphological texture features of breast pathological images, the idea of using residual network residual units to simplify the learning process and enhance gradient propagation is adopted. The deep residual network is used to divide the image into benign and malignant tumors. For medical images with billions of pixels, the strategy of randomly extracting image patches is employed for data enhancement, and the model is evaluated using summation rules. Experimental results show that the recognition accuracy of the model reaches 96%.
机译：如今，乳腺癌已成为具有高发病率和高死亡率的全球公认的肿瘤疾病。使用先进的卷积神经网络进行乳房病理图像的辅助诊断是一般趋势。由于医疗病理学图像的复杂背景和更多的噪声，双边滤波器用于降噪。针对乳房病理图像的细化，复杂性和弱化的问题，采用了使用残余网络剩余单元简化学习过程的思路，提高梯度传播。深度残余网络用于将图像划分为良性和恶性肿瘤。对于具有数十亿像素的医学图像，随机提取图像斑块的策略用于数据增强，使用求和规则来评估模型。实验结果表明，该模型的识别准确性达到96％。

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