使用深度学习方法检测皮肤癌。

IF 2.4 4区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL
Cancer Biotherapy and Radiopharmaceuticals Pub Date : 2025-06-01 Epub Date: 2025-03-28 DOI:10.1089/cbr.2024.0161
Shafiul Haque, Faraz Ahmad, Vineeta Singh, Darin Mansor Mathkor, Ashjan Babegi
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引用次数: 0

摘要

目的:本文综述了多种深度学习(DL)方法,包括人工神经网络(ann)、卷积神经网络(cnn)、k近邻网络(KNNs)以及生成对抗网络(gan),这些方法依赖于它们差分提取关键特征的能力来识别和分类皮肤病变。背景:皮肤癌是人类中最常见的癌症类型之一,并给患者和护理人员带来巨大的社会经济和心理负担。皮肤癌的发病率在过去几十年中逐渐增加。皮肤癌的早期诊断可能有助于实施更有效的治疗和治疗方案。事实上,最近的几项研究都集中在皮肤癌的早期检测策略上。其中病变的特征,可以帮助识别和表征皮肤癌的对称性,颜色,大小和形状。结果:我们的评估表明,cnn在视觉损伤识别中提供了最高的准确性,而gan已经成为通过模拟图像创建增强训练的强大工具。然而,现有数据集存在显著的局限性,例如肤色可变性不足,计算需求高,病变表征不平等,这可能会阻碍DL模型的效率、包容性和可泛化性。研究人员必须在一个结构框架内结合不同的高分辨率数据集,以开发具有无监督学习方法的高效计算模型,以增强非侵入性和精确的皮肤癌检测。结论:基于图像的计算皮肤癌检测技术的突破可能对减少侵入性诊断测试的需求和扩大皮肤癌筛查的范围至关重要,从而帮助以时间和成本效益的方式进行早期癌症检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Skin Cancer Detection Using Deep Learning Approaches.

Aim: This review examined multiple deep learning (DL) methods, including artificial neural networks (ANNs), convolutional neural networks (CNNs), k-nearest neighbors (KNNs), as well as generative adversarial networks (GANs), relying on their abilities to differentially extract key features for the identification and classification of skin lesions. Background: Skin cancer is among the most prevalent cancer types in humans and is associated with tremendous socioeconomic and psychological burdens for patients and caregivers alike. Incidences of skin cancers have progressively increased during the last decades. Early diagnoses of skin cancers may aid in the implementation of more effective treatment and therapeutic regimens. Indeed, several recent studies have focused on early detection strategies for skin cancer. Among the lesion features that can aid the recognition and characterization of skin cancers are symmetry, color, size, and shape. Results: Our assessment indicates that CNNs delivered maximum accuracy in visual lesion recognition, yet GANs have surfaced as a strong tool for training augmentation through simulated image creation. However, there were significant limitations associated with existing datasets, such as provision of insufficient skin tone variability, demanding computational needs, and unequal lesion representations, which may hamper efficiency, inclusivity, and generalizability of DL models. Researchers must combine diverse high-resolution datasets within a structural framework to develop efficient computational models with unsupervised learning methods to enhance noninvasive and precise skin cancer detection. Conclusion: The breakthroughs in image-based computational skin cancer detection may be crucial in reducing the requirement of invasive diagnostic tests and expanding the scope of skin cancer screening toward broad demographics, thereby aiding early cancer detection in a time- and cost-efficient manner.

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来源期刊
CiteScore
7.80
自引率
2.90%
发文量
87
审稿时长
3 months
期刊介绍: Cancer Biotherapy and Radiopharmaceuticals is the established peer-reviewed journal, with over 25 years of cutting-edge content on innovative therapeutic investigations to ultimately improve cancer management. It is the only journal with the specific focus of cancer biotherapy and is inclusive of monoclonal antibodies, cytokine therapy, cancer gene therapy, cell-based therapies, and other forms of immunotherapies. The Journal includes extensive reporting on advancements in radioimmunotherapy, and the use of radiopharmaceuticals and radiolabeled peptides for the development of new cancer treatments.
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