许多皮肤癌疾病具有明显的初期患病征兆. 目前皮肤癌诊断主要依靠具有专业知识的医务工作者进行诊断, 其存在着耗时长、复用性低等问题. 针对上述问题, 该研究提出一种基于改进MobileNetV3-Small的轻量级皮肤肿瘤识别模型. 首先提出了一种基于坐标注意力机制模块(coordinate attention, CA)的CaCo注意力模块. 其次针对皮肤肿瘤数据集样本分布不均衡, 提出了一种联合多损失函数来增强模型对少样本病例的学习能力. 实验结果表明, 改进的MobileNetV3-CaCo模型精确率、平衡准确性和模型参数量分别为93.39%、86.35%和2.29M, 达到了理想的识别效果.
Many skin cancer diseases have obvious early symptoms. Currently, the diagnosis of skin cancer mainly relies on medical workers with professional knowledge, bringing the problems such as long time consumption and low reusability. In response to these problems, a lightweight skin disease recognition model based on improved MobileNetV3-Small is proposed in this study. Firstly, a CaCo attention module based on coordinate attention (CA) mechanism is proposed, Secondly, for the uneven distribution of the samples of skin-cancer datasets, a combination of multiple loss functions is proposed to enhance the learning ability of the model for cases with few samples. The improved MobileNetV3-CaCo model has an accuracy, balance accuracy, and model parameter quantity of 93.39%, 86.35%, and 2.29M, respectively, achieving ideal recognition results.