Institute of Electronics, Lodz University of Technology, Żeromskiego 116, 90-924 Łódź, Poland.
Department of Mechatronics and Technical and IT Education, Faculty of Technical Science, University of Warmia and Mazury, 11-041 Olsztyn, Poland.
Sensors (Basel). 2021 Oct 6;21(19):6639. doi: 10.3390/s21196639.
Melanoma is one of the most lethal and rapidly growing cancers, causing many deaths each year. This cancer can be treated effectively if it is detected quickly. For this reason, many algorithms and systems have been developed to support automatic or semiautomatic detection of neoplastic skin lesions based on the analysis of optical images of individual moles. Recently, full-body systems have gained attention because they enable the analysis of the patient's entire body based on a set of photos. This paper presents a prototype of such a system, focusing mainly on assessing the effectiveness of algorithms developed for the detection and segmentation of lesions. Three detection algorithms (and their fusion) were analyzed, one implementing deep learning methods and two classic approaches, using local brightness distribution and a correlation method. For fusion of algorithms, detection sensitivity = 0.95 and precision = 0.94 were obtained. Moreover, the values of the selected geometric parameters of segmented lesions were calculated and compared for all algorithms. The obtained results showed a high accuracy of the evaluated parameters (error of area estimation <10%), especially for lesions with dimensions greater than 3 mm, which are the most suspected of being neoplastic lesions.
黑色素瘤是最致命和生长最快的癌症之一，每年导致许多人死亡。如果能快速发现这种癌症，就能有效地进行治疗。出于这个原因，已经开发了许多算法和系统来支持基于对单个痣的光学图像分析的肿瘤性皮肤病变的自动或半自动检测。最近，全身系统引起了人们的关注，因为它们能够基于一组照片分析患者的整个身体。本文介绍了这样一个系统的原型，主要侧重于评估为检测和分割病变而开发的算法的有效性。分析了三种检测算法（及其融合），一种实现了深度学习方法，两种是经典方法，使用局部亮度分布和相关方法。对于算法融合，检测灵敏度为 0.95，精度为 0.94。此外，还计算并比较了所有算法分割病变的选定几何参数的值。得到的结果表明评估参数的准确性很高（面积估计误差 <10%），特别是对于尺寸大于 3 毫米的病变，这些病变最有可能是肿瘤性病变。
