In the process of mitotic results of pathological images of breast cancer,due to the interference of cells with similar morphology,the target of mitotic cells is small,and it is difficult to divide and mark,thus limiting the efficiency and accuracy of breast cancer grading diagnosis and treatment.Therefore,an improved YOLOv5s based pathological image detection algorithm for breast cancer was proposed.Transformer structure is added to the backbone network to enhance the detection ability of small image targets.By introducing ACMix structure and fusing image features,the detection performance is improved,and the attention mechanism of convolutional neural network on small targets is strengthened.Add SK-attention to the detection header to ensure the accuracy of capturing small targets.The results showed that the detection performance of the improved YOLOv5s was better than that of the traditional model before the improvement,and the detection accuracy was 97.12%,which could better identify mitotic cells in pathological images of breast cancer,and thus provide decision-making basis for subsequent diagnosis and treatment.
breast cancer pathological imageYOLOv5sfeature fusionobject detection
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