Background: Skin cancer is the most common cancer worldwide, with melanoma being the deadliest type, though it accounts for less than 5% of cases. Traditional skin cancer detection methods are effective but are often costly and time-consuming. Recent advances in artificial intelligence have improved skin cancer diagnosis by helping dermatologists identify suspicious lesions. Methods: The study used datasets from two ethnic groups, sourced from the ISIC platform and CSMU Hospital, to develop an AI diagnostic model. Eight pre-trained models, including convolutional neural networks and vision transformers, were fine-tuned. The three best-performing models were combined into an ensemble model, which underwent multiple random experiments to ensure stability. To improve diagnostic accuracy and reduce false negatives, a two-stage classification strategy was employed: a three-class model for initial classification, followed by a binary model for secondary prediction of benign cases. Results: In the ISIC dataset, the false negative rate for malignant lesions was significantly reduced, and the number of malignant cases misclassified as benign dropped from 124 to 45. In the CSMUH dataset, false negatives for malignant cases were completely eliminated, reducing the number of misclassified malignant cases to zero, resulting in a notable improvement in diagnostic precision and a reduction in the false negative rate. Conclusions: Through the proposed method, the study demonstrated clear success in both datasets. First, a three-class AI model can assist doctors in distinguishing between melanoma patients who require urgent treatment, non-melanoma skin cancer patients who can be treated later, and benign cases that do not require intervention. Subsequently, a two-stage classification strategy effectively reduces false negatives in malignant lesions. These findings highlight the potential of AI technology in skin cancer diagnosis, particularly in resource-limited medical settings, where it could become a valuable clinical tool to improve diagnostic accuracy, reduce skin cancer mortality, and reduce healthcare costs.
背景：皮肤癌是全球最常见的癌症，其中黑色素瘤是最致命的类型，尽管其病例占比不足5%。传统的皮肤癌检测方法虽然有效，但通常成本高昂且耗时。人工智能的最新进展通过帮助皮肤科医生识别可疑皮损，提升了皮肤癌的诊断水平。 方法：本研究利用来自ISIC平台和CSMU医院的两个不同种族群体的数据集，开发了一种人工智能诊断模型。对包括卷积神经网络和视觉变换器在内的八个预训练模型进行了微调。将三个性能最佳的模型组合成集成模型，并通过多次随机实验确保其稳定性。为提高诊断准确性并降低假阴性率，采用了两阶段分类策略：首先使用三分类模型进行初步分类，再通过二分类模型对良性病例进行二次预测。 结果：在ISIC数据集中，恶性皮损的假阴性率显著降低，被误判为良性的恶性病例数从124例减少至45例。在CSMUH数据集中，恶性病例的假阴性被完全消除，误判恶性病例数降为零，诊断精度显著提升，假阴性率明显下降。 结论：通过所提出的方法，本研究在两个数据集中均取得了明确成功。首先，三分类人工智能模型可辅助医生区分需要紧急治疗的黑色素瘤患者、可延期治疗的非黑色素瘤皮肤癌患者以及无需干预的良性病例。随后，两阶段分类策略有效降低了恶性皮损的假阴性率。这些发现凸显了人工智能技术在皮肤癌诊断中的潜力，特别是在医疗资源有限的环境中，该技术有望成为提高诊断准确性、降低皮肤癌死亡率和减少医疗成本的重要临床工具。
肿瘤(癌症)患者之家