Skin lesion is one of the severe diseases which in many cases endanger the lives of patients on a worldwide extent. Early detection of disease in dermoscopy images can significantly increase the survival rate. However, the accurate detection of disease is highly challenging due to the following reasons: e.g., visual similarity between different classes of disease (e.g., melanoma and non-melanoma lesions), low contrast between lesions and skin, background noise, and artifacts. Machine learning models based on convolutional neural networks (CNN) have been widely used for automatic recognition of lesion diseases with high accuracy in comparison to conventional machine learning methods. In this research, we proposed a new preprocessing technique in order to extract the region of interest (RoI) of skin lesion dataset. We compare the performance of the most state-of-the-art CNN classifiers with two datasets which contain (1) raw, and (2) RoI extracted images. Our experiment results show that training CNN models by RoI extracted dataset can improve the accuracy of the prediction (e.g., InceptionResNetV2, 2.18% improvement). Moreover, it significantly decreases the evaluation (inference) and training time of classifiers as well.

皮肤病变是世界范围内在许多情况下危及患者生命的严重疾病之一。皮肤镜图像中疾病的早期检测可以显着提高存活率。然而，由于以下原因，疾病的准确检测非常具有挑战性：例如，不同类别疾病（例如黑色素瘤和非黑色素瘤病变）之间的视觉相似性、病变和皮肤之间的低对比度、背景噪声和伪影。与传统的机器学习方法相比，基于卷积神经网络（CNN）的机器学习模型已被广泛用于高精度的病变疾病自动识别。在这项研究中，我们提出了一种新的预处理技术，以提取皮肤病变数据集的感兴趣区域 (RoI)。我们将最先进的 CNN 分类器的性能与包含 (1) 原始图像和 (2) RoI 提取图像的两个数据集进行比较。我们的实验结果表明，通过 RoI 提取的数据集训练 CNN 模型可以提高预测的准确性（例如，InceptionResNetV2，提高 2.18%）。此外，它还显着减少了分类器的评估（推理）和训练时间。