论文标题
使用卷积神经网络的强度值估计，黑色素瘤皮肤癌和奈夫摩尔分类
Melanoma Skin Cancer and Nevus Mole Classification using Intensity Value Estimation with Convolutional Neural Network
论文作者
论文摘要
黑色素瘤皮肤癌是最危险和威胁生命的癌症之一。暴露于紫外线可能会损害皮肤细胞的DNA，从而导致黑色素瘤皮肤癌。但是，很难在未成熟的阶段检测和分类黑色素瘤和内华痣。在这项工作中，根据卷积神经网络模型（CNN）的强度估计而开发了自动深度学习系统，以更准确地检测和对黑色素瘤进行分类。由于强度水平是对象或感兴趣的识别区域的最独特特征，因此从提取的病变图像中选择高强度像素值。与检测黑色素瘤皮肤癌的最新方法相比，将这些高强度特征纳入CNN可以提高所提出模型的整体性能。为了评估系统，我们使用了5倍的交叉验证。实验结果表明，达到了准确性（92.58％），灵敏度（93.76％），特异性（91.56％）和精度（90.68％）的高度百分比。
Melanoma skin cancer is one of the most dangerous and life-threatening cancer. Exposure to ultraviolet rays may damage the skin cell's DNA, which causes melanoma skin cancer. However, it is difficult to detect and classify melanoma and nevus mole at the immature stages. In this work, an automatic deep learning system is developed based on the intensity value estimation with a convolutional neural network model (CNN) to detect and classify melanoma and nevus mole more accurately. Since intensity levels are the most distinctive features for object or region of interest identification, the high-intensity pixel values are selected from the extracted lesion images. Incorporating those high-intensity features into the CNN improves the overall performance of the proposed model than the state-of-the-art methods for detecting melanoma skin cancer. To evaluate the system, we used 5-fold cross-validation. Experimental results show that a superior percentage of accuracy (92.58%), sensitivity (93.76%), specificity (91.56%), and precision (90.68%) are achieved.
