自适应学习中基于CNN和IIDLA的图像识别方法研究  

Image Recognition Method based on CNN and IIDLA in Adaptive Learning

作　　者：王敏[1] WANG Min(Dean's office,Fujian Chuanzheng Communications College,Fuzhou 350007,China)

机构地区：[1]福建船政交通职业学院教务处,福建福州350007

出　　处：《吉林化工学院学报》2024年第3期56-61,共6页Journal of Jilin Institute of Chemical Technology

摘　　要：近年来计算机辅助医学进行影像诊断逐渐成了该领域的研究热点,为了更好地对医学图像特征进行分类与识别,研究以自适应学习为背景,提出一种融合卷积神经网络与改进迭代深度学习的图像识别方法。过程中引入随机化融合改进卷积神经网络,以应对医学图像的多模态特征提取,并结合改进迭代深度学习避免图像数据信息丢失,最终完成对图像信息的识别。结果显示,研究方法在训练集与验证集上进行实验,当迭代进行到第28次与第17次时,系统便开始趋于稳定,对应得到损失函数值分别为0.0124与0.0112。当四种算法的精准率为0.900时,得到的改进型深度学习模型、LeNet-5CNN模型、IYolo-v5模型以及研究方法对应的召回率分别为0.6232、0.5791、0.6774与0.8369。研究方法对5种疾病的识别准确率均明显高于95%。以上结果表示研究方法具有较快的收敛速度与精度,同时能够被广泛应用于多种类型疾病的图像诊断识别当中。In recent years,computer-assisted medical imaging diagnosis has gradually become a research hotspot in this field.In order to better classify and identify medical image features,this study proposed an image recognition method that integrates convolutional neural networks and improved iterative deep learning based on adaptive learning.In the process,a randomized fusion improved convolutional neural network was introduced to cope with the multimodal feature extraction of medical images,and combined with improved iterative deep learning to avoid the loss of image data information,and finally complete the recognition of image information.The results showed that the research method was experimented on the training set and the validation set.When the iteration was carried out to the 28th and 17th times,the system begins to stabilize,and the corresponding loss function values were 0.0124 and 0.0112 respectively.When the precision of the four algorithms was 0.900,the recall rates of the improved deep learning model,LeNet-5CNN model,IYolo-v5 model and the research method were 0.6232,0.5791,0.6774 and 0.8369 respectively.The recognition accuracy of the research method for the five diseases was significantly higher than 95%.The above results indicated that the research method has a fast convergence speed and accuracy,and could be widely used in image diagnosis and recognition of various types of diseases.

关 键 词：CNN 改进迭代深度学习 图像识别 医学 自适应学习 

分 类 号：R318[医药卫生—生物医学工程]