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
proposes 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 is
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 show that the research method is experimented on the training set
and the validation set. When the iteration is carried out to the 28th and 17th
times, the system begins to stabilize, and the corresponding loss function
values are 0.0124 and 0.0112 respectively. When the precision of the four
algorithms is 0.900, the recall rates of the improved deep learning model,
LeNet-5CNN model, IYolo-v5 model and the research method are 0.6232, 0.5791,
0.6774 and 0.8369 respectively. The recognition accuracy of the research method
for the five diseases is significantly higher than 95%. The above results
indicate that the research method has a fast convergence speed and accuracy,
and can be widely used in image diagnosis and recognition of various types of
diseases.
王敏. 自适应学习中基于CNN和IIDLA的图像识别方法研究[J]. 吉林化工学院学报, 2024, 41(3): 56-61.
WANG Min. Image Recognition Method based on CNN and IIDLA in Adaptive Learning. Journal of Jilin Institute of Chemical Technology, 2024, 41(3): 56-61.