Today, skin cancer, and especially melanoma, is an increasing and dangerous health disease. The high mortality rate of some types of skin cancers needs to be detected in the early stages and treated urgently. The use of neural network ensembles for the detection of objects of interest in images has gained more and more interest due to the increased performance of the results. In this sense, this paper proposes two ensembles of neural networks, based on the fusion of the decisions of the component neural networks for the detection of four skin lesions (basal cancer cell, melanoma, benign keratosis, and melanocytic nevi). The first system is based on separate learning of three neural networks (MobileNet V2, DenseNet 169, and EfficientNet B2), with multiple weights for the four classes of lesions and weighted overall prediction. The second system is made up of six binary models (one for each pair of classes) for each network; the fusion and prediction are conducted by weighted summation per class and per model. In total, 18 such binary models will be considered. The 91.04% global accuracy of this set of binary models is superior to the first system (89.62%). Separately, only for the binary classifications within the system was the individual accuracy better. The individual F1 score for each class and the global system varied from 81.36% to 94.17%. Finally, a critical comparison is made with similar works from the literature.
如今，皮肤癌尤其是黑色素瘤已成为日益严峻的健康威胁。某些类型皮肤癌的高死亡率要求我们必须尽早发现并紧急治疗。由于神经网络集成在图像目标检测中展现出卓越性能，其在医学图像分析领域的应用日益受到关注。基于此，本文提出两种神经网络集成系统，通过融合组件神经网络的决策结果，实现对四种皮肤病变（基底细胞癌、黑色素瘤、良性角化病和黑色素细胞痣）的检测。第一套系统基于三个独立训练的神经网络（MobileNet V2、DenseNet 169和EfficientNet B2），针对四类病变设置多重权重并采用加权整体预测。第二套系统则为每个网络构建六个二元分类模型（覆盖所有类别组合），通过按类别和模型加权求和的方式进行决策融合。该系统共包含18个二元模型，其整体准确率达到91.04%，优于第一套系统的89.62%。在系统内部二元分类任务中，单个模型准确率表现更优。各病变类别的个体F1分数及系统整体F1分数分布在81.36%至94.17%之间。最后，本文与文献中的同类研究进行了批判性比较。
Detection of Malignant Skin Lesions Based on Decision Fusion of Ensembles of Neural Networks
肿瘤(癌症)患者之家