Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review.
Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review.
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机器学习和深度学习在皮肤病诊断中的最新进展与展望：综述
DOI：
10.3390/diagnostics13233506
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发表时间：
2023-11-22
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
目的：皮肤病是一个普遍关注的健康问题，机器学习和深度学习算法的应用有助于提高诊断的准确性和治疗的有效性。本文对机器学习和深度学习在皮肤病诊断领域中的应用进行了综述，重点介绍了近年来广泛使用的深度学习方法。还分析了目前的挑战和制约因素，并提出了可能的解决办法。方法：我们从包括IEEE、Springer、Web of Science和PubMed在内的著名数据库中收集全面的文献，特别强调最近5年的进展。从广泛的现有研究语料库中，29篇与皮肤病图像分割相关的文章和45篇关于皮肤病图像分类的文章被纳入本综述。根据所使用的计算算法，这些文章被系统地分为两类：传统的机器学习算法和深度学习算法。根据所采用的方法及其相应结果，进行了深入的比较分析。结论：目前的研究结果强调了深度学习方法在皮肤病诊断领域中比传统机器学习技术更有效。尽管如此，仍有很大的改进余地，特别是在提高算法的准确性方面。与各种数据集的可用性、分类和分类模型的概括性以及模型的可解释性相关的挑战也仍然是紧迫的问题。此外，未来的研究重点应该适当转移。现有的大量研究主要集中在黑色素瘤上，因此未来有必要拓宽着色性皮肤病的研究领域。这些见解不仅强调了深度学习在皮肤病诊断中的潜力，也强调了应该关注的方向。
Objective: Skin diseases constitute a widespread health concern, and the application of machine learning and deep learning algorithms has been instrumental in improving diagnostic accuracy and treatment effectiveness. This paper aims to provide a comprehensive review of the existing research on the utilization of machine learning and deep learning in the field of skin disease diagnosis, with a particular focus on recent widely used methods of deep learning. The present challenges and constraints were also analyzed and possible solutions were proposed. Methods: We collected comprehensive works from the literature, sourced from distinguished databases including IEEE, Springer, Web of Science, and PubMed, with a particular emphasis on the most recent 5-year advancements. From the extensive corpus of available research, twenty-nine articles relevant to the segmentation of dermatological images and forty-five articles about the classification of dermatological images were incorporated into this review. These articles were systematically categorized into two classes based on the computational algorithms utilized: traditional machine learning algorithms and deep learning algorithms. An in-depth comparative analysis was carried out, based on the employed methodologies and their corresponding outcomes. Conclusions: Present outcomes of research highlight the enhanced effectiveness of deep learning methods over traditional machine learning techniques in the field of dermatological diagnosis. Nevertheless, there remains significant scope for improvement, especially in improving the accuracy of algorithms. The challenges associated with the availability of diverse datasets, the generalizability of segmentation and classification models, and the interpretability of models also continue to be pressing issues. Moreover, the focus of future research should be appropriately shifted. A significant amount of existing research is primarily focused on melanoma, and consequently there is a need to broaden the field of pigmented dermatology research in the future. These insights not only emphasize the potential of deep learning in dermatological diagnosis but also highlight directions that should be focused on.
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影响因子：
3.4
作者：
Liu, Lin;Liang, Chen;Xue, Yuzhou;Chen, Tingqiao;Chen, Yangmei;Lan, Yufan;Wen, Jiamei;Shao, Xinyi;Chen, Jin
通讯作者：
Chen, Jin
影响因子：
1.3
作者：
Bozorgtabar, B.;Sedai, S.;Garnavi, R.
通讯作者：
Garnavi, R.
DOI：
10.1186/s42492-021-00091-z
发表时间：
2021-10-07
影响因子：
--
作者：
Abbas Q;Ramzan F;Ghani MU
通讯作者：
Ghani MU
DOI：
10.3390/s21175846
发表时间：
2021-08-30
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Czajkowska J;Badura P;Korzekwa S;Płatkowska-Szczerek A;Słowińska M
通讯作者：
Słowińska M
影响因子：
0.9
作者：
Burlando M;Muracchioli A;Cozzani E;Parodi A
通讯作者：
Parodi A