Image Recognition Method based on CNN and IIDLA in Adaptive Learning
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 0.012 4 and 0.011 2 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 0.623 2,0.579 1,0.677 4 and 0.836 9 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.
万方数据
维普
