文章：

基于统一深度卷积神经网络的皮肤癌识别研究

Skin Cancer Recognition Using Unified Deep Convolutional Neural Networks

原文发布日期：22 March 2024

DOI: 10.3390/cancers16071246

类型: Article

开放获取: 是

英文摘要：

The incidence of skin cancer is rising globally, posing a significant public health threat. An early and accurate diagnosis is crucial for patient prognoses. However, discriminating between malignant melanoma and benign lesions, such as nevi and keratoses, remains a challenging task due to their visual similarities. Image-based recognition systems offer a promising solution to aid dermatologists and potentially reduce unnecessary biopsies. This research investigated the performance of four unified convolutional neural networks, namely, YOLOv3, YOLOv4, YOLOv5, and YOLOv7, in classifying skin lesions. Each model was trained on a benchmark dataset, and the obtained performances were compared based on lesion localization, classification accuracy, and inference time. In particular, YOLOv7 achieved superior performance with an Intersection over Union (IoU) of 86.3%, a mean Average Precision (mAP) of 75.4%, an F1-measure of 80%, and an inference time of 0.32 s per image. These findings demonstrated the potential of YOLOv7 as a valuable tool for aiding dermatologists in early skin cancer diagnosis and potentially reducing unnecessary biopsies.

摘要翻译： 

皮肤癌的全球发病率持续上升，已成为重大公共卫生威胁。早期准确诊断对患者预后至关重要。然而，由于恶性黑色素瘤与痣、角化病等良性皮损在视觉上具有高度相似性，鉴别诊断仍面临挑战。基于图像的识别系统为辅助皮肤科医生诊断、减少不必要活检提供了可行方案。本研究评估了YOLOv3、YOLOv4、YOLOv5和YOLOv7四种统一卷积神经网络在皮肤病变分类中的性能。各模型均基于基准数据集进行训练，并从病变定位、分类准确率和推理时间三个维度比较其表现。结果显示，YOLOv7模型表现最优，其交并比达86.3%，平均精度均值75.4%，F1分数80%，单张图像推理时间仅需0.32秒。这些发现表明YOLOv7可作为辅助皮肤科医生实现早期皮肤癌诊断、减少不必要活检的有效工具。

原文链接：

Skin Cancer Recognition Using Unified Deep Convolutional Neural Networks