Malaria detection based on ResNet + CBAM attention mechanism

Aiming at the low accuracy and time-consuming training of malaria detection, this paper proposes a malaria detection algorithm based on ResNet+CBAM attention mechanism. In the ResNet-40 model, which reduces the number of network layers and network width, the CBAM attention mechanism module is added and trained on the malaria dataset (Malaria dataset). The experimental results show that the detection method proposed in this paper improves the classification accuracy by 1% on the original basis.