卷积神经网络在图像识别中的应用

Application of Convolutional Neural Network in Image Recognition

随着医学成像技术的不断发展,病理识别在医学诊断过程中的作用越来越重要。人工智能领域的机器学习可以帮助完成医学图像诊断的自动识别,数字化地辅助医学诊断过程,同时降低医务工作者的工作量。卷积神经网络(CNN)是近年发展起来的一种非常有效的机器学习方法,属于深度学习的范畴,它能够完整地模拟人类的图像识别过程,并且已经在图像识别领域取得了优异的成绩。本文将卷积神经网络应用于病理图像的识别中,同时对病理图片进行了采集、整理和智能学习,完成并分析了算法对比实验,最终实现了对病理图像的优化识别,提高了病理图像的识别率,验证了算法的有效性。

With the continuous development of medical imaging technology,pathological identification plays an increasingly important role in the process of medical diagnosis.Machine learning in the field of artificial intelligence can help complete the automatic recognition of medical image diagnosis,digitally assisting the process of medical diagnosis,and reducing the workload of medical workers.Convolutional neural network(CNN)is a very effective machine learning method developed in recent years.It belongs to the category of deep learning.It can completely simulate the human image recognition process,and has achieved excellent results in the field of image recognition.In this paper,the convolutional neural network is applied to the recognition of pathological images.At the same time,the pathological images are collected,sorted and intelligently learned.The comparative experiments of the algorithms are completed and analyzed.Finally,the optimal recognition of pathological images is realized,the recognition rate of pathological images is improved,and the validity of the algorithm is verified.