Department of Computer Science, National College of Business Administration & Economics Sub Campus Multan, Multan 60000, Pakistan.
Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan.
Sensors (Basel). 2023 Oct 13;23(20):8457. doi: 10.3390/s23208457.
Skin cancer is considered a dangerous type of cancer with a high global mortality rate. Manual skin cancer diagnosis is a challenging and time-consuming method due to the complexity of the disease. Recently, deep learning and transfer learning have been the most effective methods for diagnosing this deadly cancer. To aid dermatologists and other healthcare professionals in classifying images into melanoma and nonmelanoma cancer and enabling the treatment of patients at an early stage, this systematic literature review (SLR) presents various federated learning (FL) and transfer learning (TL) techniques that have been widely applied. This study explores the FL and TL classifiers by evaluating them in terms of the performance metrics reported in research studies, which include true positive rate (TPR), true negative rate (TNR), area under the curve (AUC), and accuracy (ACC). This study was assembled and systemized by reviewing well-reputed studies published in eminent fora between January 2018 and July 2023. The existing literature was compiled through a systematic search of seven well-reputed databases. A total of 86 articles were included in this SLR. This SLR contains the most recent research on FL and TL algorithms for classifying malignant skin cancer. In addition, a taxonomy is presented that summarizes the many malignant and non-malignant cancer classes. The results of this SLR highlight the limitations and challenges of recent research. Consequently, the future direction of work and opportunities for interested researchers are established that help them in the automated classification of melanoma and nonmelanoma skin cancers.
皮肤癌被认为是一种具有高全球死亡率的危险癌症类型。由于疾病的复杂性，手动皮肤癌诊断是一种具有挑战性和耗时的方法。最近，深度学习和迁移学习已成为诊断这种致命癌症的最有效方法。为了帮助皮肤科医生和其他医疗保健专业人员将图像分类为黑色素瘤和非黑色素瘤癌症，并能够在早期治疗患者，本系统文献综述 (SLR) 介绍了已广泛应用的各种联邦学习 (FL) 和迁移学习 (TL) 技术。本研究通过评估研究报告中报告的性能指标（包括真阳性率 (TPR)、真阴性率 (TNR)、曲线下面积 (AUC) 和准确性 (ACC)）来探索 FL 和 TL 分类器。这项研究是通过回顾 2018 年 1 月至 2023 年 7 月在著名论坛上发表的声誉良好的研究来组装和系统化的。通过对七个声誉良好的数据库进行系统搜索，编制了现有文献。这项 SLR 共包含 86 篇文章。本 SLR 包含了最新的关于恶性皮肤癌分类的 FL 和 TL 算法研究。此外，还提出了一个分类法，总结了许多恶性和非恶性癌症类别。这项 SLR 的结果强调了最近研究的局限性和挑战。因此，为有兴趣的研究人员确定了工作的未来方向和机会，这有助于他们对黑色素瘤和非黑色素瘤皮肤癌进行自动分类。
