Abstract
Melting curve image is a hallmark of quantitative polymerase chain reaction and is a crucial indicator for the validity of the cycle threshold. Current mainstream methods concentrate on analyzing the melting curve images via artificial process. Therefore, we design a novel multi-scale attention residual network, leveraging various levels space features for accurately classifying the melting curve images. Two modular components are designed in our algorithm. A multi-scale feature extraction module that consists of multi-parallel attention resnet units to selectively capture close related information from various scale feature maps while a series of adaptive multi-scale fusion modules to complete cross-subnet fusion of information. In addition, we also collect massive fluorescence signal data to draw melting curve images for constructing a novel dataset. Our method is evaluated on 3 different benchmark datasets including the self-constructed melting curve image dataset, heartbeat signal dataset and natural color image dataset, a significant highlight is that it achieves a 2.0% accuracy improvement over state-of-the-art in average.
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Data Availability
The datasets analysed during the current study are available from the corresponding author on reasonable request.
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This research is supported by the National Natural Science Foundation of China (61876070).
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Su, P., Shen, X., Chen, H. et al. M-AResNet: a novel multi-scale attention residual network for melting curve image classification. Multimed Tools Appl 82, 42961–42976 (2023). https://doi.org/10.1007/s11042-023-14694-6
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DOI: https://doi.org/10.1007/s11042-023-14694-6