基于集成学习的皮肤病变检测系统，提升儿童特应性皮炎诊断准确性。
Implementation of a Skin Lesion Detection System for Managing Children with Atopic Dermatitis Based on Ensemble Learning
- 融合多个深度学习模型，通过集成学习提升诊断精度。
- 在真实用户拍摄图像上实现超过90%的召回率，处理速度低于1秒。
- 适合临床医生辅助诊断，尤其适用于缺乏专业设备的场景。
韩国《数据3法案》修订及新冠疫情推动了数字医疗市场发展，促进了人工智能在医疗数据中的应用。特应性皮炎是一种慢性炎症性皮肤病，目前依赖主观评估进行诊断，缺乏客观方法，易导致误诊，且外观与银屑病相似，进一步增加诊断难度。现有研究多基于高质量皮肤镜图像数据集，但此类图像在实际临床中难以获取。同时，现有系统需兼顾高准确率与快速响应。为此，本文提出基于集成学习的皮肤病变检测系统（ENSEL），通过集成多种深度学习模型提升诊断准确率。利用真实用户拍摄的皮肤病变图像进行检测实验，随机抽取皮肤疾病图像测试其性能。结果显示，ENSEL在多数图像上实现高召回率，处理速度小于1秒。本研究有助于实现皮肤病变的客观诊断，推动数字医疗发展。
原文摘要 · Abstract (English)
The amendments made to the Data 3 Act and impact of COVID-19 have fostered the growth of digital healthcare market and promoted the use of medical data in artificial intelligence in South Korea. Atopic dermatitis, a chronic inflammatory skin disease, is diagnosed via subjective evaluations without using objective diagnostic methods, thereby increasing the risk of misdiagnosis. It is also similar to psoriasis in appearance, further complicating its accurate diagnosis. Existing studies on skin diseases have used high-quality dermoscopic image datasets, but such high-quality images cannot be obtained in actual clinical settings. Moreover, existing systems must ensure accuracy and fast response times. To this end, an ensemble learning-based skin lesion detection system (ENSEL) was proposed herein. ENSEL enhanced diagnostic accuracy by integrating various deep learning models via an ensemble approach. Its performance was verified by conducting skin lesion detection experiments using images of skin lesions taken by actual users. Its accuracy and response time were measured using randomly sampled skin disease images. Results revealed that ENSEL achieved high recall in most images and less than 1s s processing speed. This study contributes to the objective diagnosis of skin lesions and promotes the advancement of digital healthcare.
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