Abstract：
A skin lesion is a portion of skin that observes abnormal growth compared to other areas of the skin. The ISIC 2018 lesion dataset has seven classes. A miniature dataset version of it is also available with only two classes: malignant and benign. Malignant tumors are tumors that are cancerous, and benign tumors are non-cancerous. Malignant tumors have the ability to multiply and spread throughout the body at a much faster rate. The early detection of the cancerous skin lesion is crucial for the survival of the patient. Deep learning models and machine learning models play an essential role in the detection of skin lesions. Still, due to image occlusions and imbalanced datasets, the accuracies have been compromised so far. In this paper, we introduce an interpretable method for the non-invasive diagnosis of melanoma skin cancer using deep learning and ensemble stacking of machine learning models. The dataset used to train the classifier models contains balanced images of benign and malignant skin moles. Hand-crafted features are used to train the base models (logistic regression, SVM, random forest, KNN, and gradient boosting machine) of machine learning. The prediction of these base models was used to train level one model stacking using cross-validation on the training set. Deep learning models (MobileNet, Xception, ResNet50, ResNet50V2, and DenseNet121) were used for transfer learning, and were already pre-trained on ImageNet data. The classifier was evaluated for each model. The deep learning models were then ensembled with different combinations of models and assessed. Furthermore, shapely adaptive explanations are used to construct an interpretability approach that generates heatmaps to identify the parts of an image that are most suggestive of the illness. This allows dermatologists to understand the results of our model in a way that makes sense to them. For evaluation, we calculated the accuracy, F1-score, Cohen\'s kappa, confusion matrix, and ROC curves and identified the best model for classifying skin lesions.
摘要：
皮肤损伤是与皮肤的其他区域相比观察到异常生长的皮肤部分。ISIC2018病变数据集有七个类别。它的微型数据集版本也只有两类：恶性和良性。恶性肿瘤是癌性肿瘤，良性肿瘤是非癌性的。恶性肿瘤能够以更快的速度繁殖和扩散到全身。癌性皮肤病变的早期检测对于患者的生存至关重要。深度学习模型和机器学习模型在皮肤病变的检测中起着至关重要的作用。尽管如此，由于图像遮挡和不平衡的数据集，到目前为止，准确性已经受到影响。在本文中,我们介绍了一种使用深度学习和机器学习模型集成堆叠的黑色素瘤皮肤癌非侵入性诊断的可解释方法。用于训练分类器模型的数据集包含良性和恶性皮肤痣的平衡图像。手工制作的特征用于训练基础模型(逻辑回归，SVM,随机森林,KNN,和梯度提升机)的机器学习。这些基础模型的预测用于在训练集上使用交叉验证来训练一级模型堆叠。深度学习模型(MobileNet、Xception,ResNet50、ResNet50V2和DenseNet121)用于迁移学习，并且已经对ImageNet数据进行了预训练。对每个模型评估分类器。然后将深度学习模型与不同的模型组合进行整合并进行评估。此外，形状自适应的解释用于构建可解释性方法，该方法生成热图以识别图像中最提示疾病的部分。这使皮肤科医生能够以对他们有意义的方式理解我们模型的结果。为了评估，我们计算了准确度，F1分数，科恩的卡帕，混淆矩阵，和ROC曲线，并确定了皮肤病变分类的最佳模型。
