Application of attention-based Siamese composite neural network in medical image recognition
This work addresses the challenge of insufficient data in medical image recognition, which is incremental as it combines existing attention and Siamese network techniques for a specific domain.
The study tackled the problem of few-shot and fine-grained medical image recognition by developing an attention-based Siamese neural network model, which showed improved performance over ordinary neural networks, especially with fewer image samples, as tested on Covid-19 lung samples.
Medical image recognition often faces the problem of insufficient data in practical applications. Image recognition and processing under few-shot conditions will produce overfitting, low recognition accuracy, low reliability and insufficient robustness. It is often the case that the difference of characteristics is subtle, and the recognition is affected by perspectives, background, occlusion and other factors, which increases the difficulty of recognition. Furthermore, in fine-grained images, the few-shot problem leads to insufficient useful feature information in the images. Considering the characteristics of few-shot and fine-grained image recognition, this study has established a recognition model based on attention and Siamese neural network. Aiming at the problem of few-shot samples, a Siamese neural network suitable for classification model is proposed. The Attention-Based neural network is used as the main network to improve the classification effect. Covid- 19 lung samples have been selected for testing the model. The results show that the less the number of image samples are, the more obvious the advantage shows than the ordinary neural network.