Application of attention-based Siamese composite neural network in medical image recognition
arxiv(2023)
摘要
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.
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