论文标题
带有全包智能手机的皮肤癌诊断
Skin Cancer Diagnostics with an All-Inclusive Smartphone Application
论文作者
论文摘要
在不同类型的皮肤癌中，黑色素瘤被认为是最致命的，在晚期很难治疗。在较早阶段发现黑色素瘤可能导致死亡率降低。已经开发了基于桌面的计算机辅助系统，以帮助皮肤科医生早期诊断。然而，人们对开发可移植的，家庭黑色素瘤诊断系统的兴趣很大，该系统可以评估癌性皮肤病变的风险。在这里，我们提出了一个智能手机应用程序，该应用程序将图像捕获功能与预处理和细分结合在一起，以提取皮肤病变的不对称性，边界不规则性，颜色变种和直径（ABCD）特征。使用功能集，通过支持向量机分类器来实现恶性肿瘤的分类。通过在各个数据处理阶段使用自适应算法，我们的方法在计算上是轻巧，用户友好且可靠的，可以将黑色素瘤病例与良性病例区分开。皮肤病变的图像要么用智能手机摄像头捕获，要么是从公共数据集中导入的。从图像捕获到分类的整个过程都在配备有可拆卸10倍镜头的Android智能手机上运行，并在不到一秒钟的时间内处理图像。在公共数据库中评估了200张具有合成少数群体过采样技术（SMOTE）的公共数据库（80％敏感性，90％的特异性，88％的精度和0.85面积（AUC）（AUC）（AUC）（AUC）），没有SMOTE（55％的敏感性，95％的敏感性，90％的精度，90％精度，0.75 AUC）。评估的性能指标和计算时间比以前的方法可比或更好。此包含包含的全包智能手机应用程序旨在为最终用户易于下载且易于启动，这对于此类医学诊断系统的最终民主化至关重要。
Among the different types of skin cancer, melanoma is considered to be the deadliest and is difficult to treat at advanced stages. Detection of melanoma at earlier stages can lead to reduced mortality rates. Desktop-based computer-aided systems have been developed to assist dermatologists with early diagnosis. However, there is significant interest in developing portable, at-home melanoma diagnostic systems which can assess the risk of cancerous skin lesions. Here, we present a smartphone application that combines image capture capabilities with preprocessing and segmentation to extract the Asymmetry, Border irregularity, Color variegation, and Diameter (ABCD) features of a skin lesion. Using the feature sets, classification of malignancy is achieved through support vector machine classifiers. By using adaptive algorithms in the individual data-processing stages, our approach is made computationally light, user friendly, and reliable in discriminating melanoma cases from benign ones. Images of skin lesions are either captured with the smartphone camera or imported from public datasets. The entire process from image capture to classification runs on an Android smartphone equipped with a detachable 10x lens, and processes an image in less than a second. The overall performance metrics are evaluated on a public database of 200 images with Synthetic Minority Over-sampling Technique (SMOTE) (80% sensitivity, 90% specificity, 88% accuracy, and 0.85 area under curve (AUC)) and without SMOTE (55% sensitivity, 95% specificity, 90% accuracy, and 0.75 AUC). The evaluated performance metrics and computation times are comparable or better than previous methods. This all-inclusive smartphone application is designed to be easy-to-download and easy-to-navigate for the end user, which is imperative for the eventual democratization of such medical diagnostic systems.
