R & D Setups, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan.
Department of Computer Science, COMSATS University Islamabad, Islamabad 45550, Pakistan.
Sensors (Basel). 2023 Mar 28;23(7):3548. doi: 10.3390/s23073548.
Currently, Internet of medical things-based technologies provide a foundation for remote data collection and medical assistance for various diseases. Along with developments in computer vision, the application of Artificial Intelligence and Deep Learning in IOMT devices aids in the design of effective CAD systems for various diseases such as melanoma cancer even in the absence of experts. However, accurate segmentation of melanoma skin lesions from images by CAD systems is necessary to carry out an effective diagnosis. Nevertheless, the visual similarity between normal and melanoma lesions is very high, which leads to less accuracy of various traditional, parametric, and deep learning-based methods. Hence, as a solution to the challenge of accurate segmentation, we propose an advanced generative deep learning model called the Conditional Generative Adversarial Network (cGAN) for lesion segmentation. In the suggested technique, the generation of segmented images is conditional on dermoscopic images of skin lesions to generate accurate segmentation. We assessed the proposed model using three distinct datasets including DermQuest, DermIS, and ISCI2016, and attained optimal segmentation results of 99%, 97%, and 95% performance accuracy, respectively.
目前，基于物联网的技术为各种疾病的远程数据收集和医疗援助提供了基础。随着计算机视觉的发展，人工智能和深度学习在 IOMT 设备中的应用有助于设计用于黑色素瘤等各种疾病的有效 CAD 系统，即使没有专家的情况下也可以。然而，CAD 系统要对皮肤病变图像进行有效的诊断，就必须对黑色素瘤皮肤病变进行准确的分割。然而，正常和黑色素瘤病变之间的视觉相似性非常高，这导致各种传统的、参数化的和基于深度学习的方法的准确性降低。因此，为了解决准确分割的挑战，我们提出了一种名为条件生成对抗网络（cGAN）的先进生成式深度学习模型，用于病变分割。在提出的技术中，分割图像的生成条件是基于皮肤病变的皮肤镜图像，以生成准确的分割。我们使用包括 DermQuest、DermIS 和 ISCI2016 在内的三个不同数据集来评估所提出的模型，分别获得了 99%、97%和 95%的最佳分割结果的性能精度。
