基于混合深度模型和长短期记忆的皮肤癌分类

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL
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引用次数: 0

摘要

皮肤癌分类是皮肤病学和肿瘤学的一个重要课题,因为它为皮肤癌的诊断和管理以及研究和宣传工作提供了一个框架。基于深度学习的方法可以自动处理大量图像,无需干预,从而提高皮肤癌分类的效率和可扩展性。所提出的方法结合了 ResNet50 深度模型和长短期记忆(LSTM)网络来处理连续数据,并更好地表示病变纹理的结构内容,以克服基于深度学习的分类算法的局限性。这种混合深度分类器被命名为 ResNet50-LSTM,它利用了两种深度网络的优点以及迁移学习技术,该技术允许新模型从预先训练好的模型开始,并针对特定任务对其进行微调。本文展示了三种方案,第一种是 ResNet50,第二种是结合迁移学习技术的 ResNet50(ResNet50-TL),第三种是(ResNet50-LSTM-TL)深度模型。将 ResNet50、LSTM 和迁移学习技术相结合可以提高皮肤癌分类的性能,使模型能够利用来自大型数据集的预训练特征,分析医学图像中的序列特征,并针对皮肤癌分类的特定任务对其进行微调。这些场景的性能与其他深度学习模型进行了比较。研究结果表明,所提出的第三种方案成功地准确识别了各种皮肤癌,准确率超过 99.09%,令人印象深刻。研究结果表明,所提出的算法有可能通过提高准确率和效率来显著增强皮肤癌分类能力。
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 classification based on a hybrid deep model and long short-term memory
Skin cancer classification is an important topic in dermatology and oncology because it provides a framework for diagnosing and managing skin cancer, as well as for research and advocacy efforts. Deep learning-based methods have the potential to improve the efficiency and scalability of skin cancer classification by automatically processing large volumes of images without the need for intervention. The proposed method combines the ResNet50 deep model and long short-term memory (LSTM) network to process sequential data and represent the structural content of lesion texture better to overcome the limitations of a deep learning-based classification algorithm. This hybrid deep classifier, named ResNet50-LSTM, takes advantage of the benefits of both deep networks along with a transfer learning technique which allows a new model to start from a pre-trained model and fine-tune it for the specific task. Three scenarios are demonstrated in this paper that consists, the first one, ResNet50, the second one ResNet50 in combination with transfer learning technique (ResNet50-TL), and the third scenario, (ResNet50-LSTM-TL) deep model. Combining ResNet50, LSTM, and transfer learning techniques can improve the performance of skin cancer classification by allowing the model to take advantage of pre-trained features from a large dataset, analyze sequential features in medical images, and fine-tune them for the specific task of skin cancer classification. The performance of these scenarios is compared with the other deep learning models. The results of the conducted study demonstrate that the proposed third scenario is successful in accurately recognizing various skin cancers, with an impressive accuracy rate of over 99.09%. The findings indicate that the proposed algorithm has the potential to significantly enhance skin cancer classification and by improving their accuracy and efficiency.
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来源期刊
Biomedical Signal Processing and Control
Biomedical Signal Processing and Control 工程技术-工程:生物医学
CiteScore
9.80
自引率
13.70%
发文量
822
审稿时长
4 months
期刊介绍: Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.
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