计算机视觉和深度学习的进展促进了黑色素瘤的早期检测。

IF 2.5 3区 生物学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Yantong Liu, Chuang Li, Feifei Li, Rubin Lin, Dongdong Zhang, Yifan Lian
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引用次数: 0

摘要

黑色素瘤的特点是其进展迅速和死亡率高,因此早期和准确的检测对于改善患者的预后至关重要。本文全面回顾了早期黑色素瘤检测的重大进展,重点是集成计算机视觉和深度学习技术。本研究对YOLO、GAN、Mask R-CNN、ResNet、DenseNet等前沿神经网络进行研究,探讨其在增强黑色素瘤早期检测和诊断中的应用。这些模型因其增强皮肤影像学和诊断准确性的能力而受到严格评估,这对于有效治疗黑色素瘤至关重要。我们的研究表明,这些人工智能技术改进了图像分析和特征提取,并增强了各种临床环境的处理能力。此外,我们强调综合皮肤病学数据集的重要性,如PH2, ISIC, DERMQUEST和MED-NODE,这对于训练和验证这些复杂的模型至关重要。整合这些数据集确保了人工智能系统的鲁棒性、通用性,并在不同条件下表现良好。这项研究的结果表明,将人工智能整合到黑色素瘤检测中,标志着医疗诊断领域的重大进步,有望通过更准确、更早的检测方法改善患者的治疗效果。未来的研究应侧重于进一步增强这些技术,整合多模式数据,提高人工智能决策的可解释性,以促进临床应用,从而将黑色素瘤诊断转变为更精确、个性化和预防性的医疗保健服务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advances in computer vision and deep learning-facilitated early detection of melanoma.

Melanoma is characterized by its rapid progression and high mortality rates, making early and accurate detection essential for improving patient outcomes. This paper presents a comprehensive review of significant advancements in early melanoma detection, with a focus on integrating computer vision and deep learning techniques. This study investigates cutting-edge neural networks such as YOLO, GAN, Mask R-CNN, ResNet, and DenseNet to explore their application in enhancing early melanoma detection and diagnosis. These models were critically evaluated for their capacity to enhance dermatological imaging and diagnostic accuracy, crucial for effective melanoma treatment. Our research demonstrates that these AI technologies refine image analysis and feature extraction, and enhance processing capabilities in various clinical settings. Additionally, we emphasize the importance of comprehensive dermatological datasets such as PH2, ISIC, DERMQUEST, and MED-NODE, which are crucial for training and validating these sophisticated models. Integrating these datasets ensures that the AI systems are robust, versatile, and perform well under diverse conditions. The results of this study suggest that the integration of AI into melanoma detection marks a significant advancement in the field of medical diagnostics and is expected to have the potential to improve patient outcomes through more accurate and earlier detection methods. Future research should focus on enhancing these technologies further, integrating multimodal data, and improving AI decision interpretability to facilitate clinical adoption, thus transforming melanoma diagnostics into a more precise, personalized, and preventive healthcare service.

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来源期刊
Briefings in Functional Genomics
Briefings in Functional Genomics BIOTECHNOLOGY & APPLIED MICROBIOLOGY-GENETICS & HEREDITY
CiteScore
6.30
自引率
2.50%
发文量
37
审稿时长
6-12 weeks
期刊介绍: Briefings in Functional Genomics publishes high quality peer reviewed articles that focus on the use, development or exploitation of genomic approaches, and their application to all areas of biological research. As well as exploring thematic areas where these techniques and protocols are being used, articles review the impact that these approaches have had, or are likely to have, on their field. Subjects covered by the Journal include but are not restricted to: the identification and functional characterisation of coding and non-coding features in genomes, microarray technologies, gene expression profiling, next generation sequencing, pharmacogenomics, phenomics, SNP technologies, transgenic systems, mutation screens and genotyping. Articles range in scope and depth from the introductory level to specific details of protocols and analyses, encompassing bacterial, fungal, plant, animal and human data. The editorial board welcome the submission of review articles for publication. Essential criteria for the publication of papers is that they do not contain primary data, and that they are high quality, clearly written review articles which provide a balanced, highly informative and up to date perspective to researchers in the field of functional genomics.
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