利用动态可扩展表示法开发和验证适应性皮肤癌分类系统

IF 2.3 Q3 MEDICAL INFORMATICS
Healthcare Informatics Research Pub Date : 2024-04-01 Epub Date: 2024-04-30 DOI:10.4258/hir.2024.30.2.140
Bong Kyung Jang, Yu Rang Park
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引用次数: 0

摘要

目的:皮肤癌是一种常见的恶性肿瘤,需要高效的诊断工具。本研究旨在利用动态可扩展表示(DER)增量学习算法开发一种自动皮肤病变分类模型。该算法能适应新数据并扩展其分类能力,目的是创建一个可扩展的高效皮肤癌诊断系统:具有增量学习功能的 DER 模型应用于 HAM10000 和 ISIC 2019 数据集。验证包括两个步骤:首先,根据固定的 ResNet-50 对 HAM10000 数据集进行训练和评估;然后,使用 ISIC 2019 数据集对训练好的模型进行外部验证。使用精确度、召回率、F1-分数和精确度-召回率曲线下面积评估模型的性能:结果:所开发的皮损分类模型在各种类型的皮损中均表现出较高的准确性和可靠性,加权平均精确度、召回率和 F1 分数分别为 0.918、0.808 和 0.847。该模型的平均曲线下面积(AUC)值为 0.943,反映了其鉴别性能。利用 ISIC 2019 数据集进行的进一步外部验证证实了该模型的有效性,AUC 值为 0.911:本研究提出了一种基于 DER 算法的优化皮肤病变分类模型,该模型在疾病分类方面表现出很高的性能,并有可能扩大其分类范围。该模型在外部验证中表现出稳健的结果,表明其对新疾病类别的适应性很强。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and Validation of Adaptable Skin Cancer Classification System Using Dynamically Expandable Representation.

Objectives: Skin cancer is a prevalent type of malignancy, necessitating efficient diagnostic tools. This study aimed to develop an automated skin lesion classification model using the dynamically expandable representation (DER) incremental learning algorithm. This algorithm adapts to new data and expands its classification capabilities, with the goal of creating a scalable and efficient system for diagnosing skin cancer.

Methods: The DER model with incremental learning was applied to the HAM10000 and ISIC 2019 datasets. Validation involved two steps: initially, training and evaluating the HAM10000 dataset against a fixed ResNet-50; subsequently, performing external validation of the trained model using the ISIC 2019 dataset. The model's performance was assessed using precision, recall, the F1-score, and area under the precision-recall curve.

Results: The developed skin lesion classification model demonstrated high accuracy and reliability across various types of skin lesions, achieving a weighted-average precision, recall, and F1-score of 0.918, 0.808, and 0.847, respectively. The model's discrimination performance was reflected in an average area under the curve (AUC) value of 0.943. Further external validation with the ISIC 2019 dataset confirmed the model's effectiveness, as shown by an AUC of 0.911.

Conclusions: This study presents an optimized skin lesion classification model based on the DER algorithm, which shows high performance in disease classification with the potential to expand its classification range. The model demonstrated robust results in external validation, indicating its adaptability to new disease classes.

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来源期刊
Healthcare Informatics Research
Healthcare Informatics Research MEDICAL INFORMATICS-
CiteScore
4.90
自引率
6.90%
发文量
44
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