论文标题
自动皮肤癌分类的类加权和局灶性损失功能的转移学习
Transfer learning with class-weighted and focal loss function for automatic skin cancer classification
论文作者
论文摘要
皮肤癌在世界上最常见的癌症的前三名中。在不同的皮肤癌类型中，黑色素瘤的转移能力特别危险。早期发现是皮肤癌治疗成功的关键。然而，由于良性和恶性病变之间的相似之处，皮肤癌诊断仍然是一个挑战，即使对于经验丰富的皮肤科医生来说，诊断也是一个挑战。为了帮助皮肤癌诊断中的皮肤科医生，我们开发了一种深度学习系统，可以有效并自动将皮肤病变分类为七个类别之一：（1）活化性角膜结构，（2）基底细胞癌，（3）良性角化症，（3）良性角化瘤，（4）（4）Dermatofibroma，（4）皮肤病，（5）梅拉诺环瘤（5）梅拉诺环瘤（6）sriman vastic vastic vastic，（6）liran melany（7）（7）（7）（7）（7）（7）（7）（7）（7）。 HAM10000数据集用于训练系统。端到端的深度学习过程，传输学习技术，利用多个预训练的模型，与分类过程相结合。结果是，我们修改的RESNET50型号的合奏可以将皮肤病变分别为七个类别之一，其中TOP-1，TOP-2和TOP-3精度分别为93％，97％和99％。该深度学习系统可能会纳入支持皮肤癌诊断的皮肤科医生的计算机辅助诊断系统。
Skin cancer is by far in top-3 of the world's most common cancer. Among different skin cancer types, melanoma is particularly dangerous because of its ability to metastasize. Early detection is the key to success in skin cancer treatment. However, skin cancer diagnosis is still a challenge, even for experienced dermatologists, due to strong resemblances between benign and malignant lesions. To aid dermatologists in skin cancer diagnosis, we developed a deep learning system that can effectively and automatically classify skin lesions into one of the seven classes: (1) Actinic Keratoses, (2) Basal Cell Carcinoma, (3) Benign Keratosis, (4) Dermatofibroma, (5) Melanocytic nevi, (6) Melanoma, (7) Vascular Skin Lesion. The HAM10000 dataset was used to train the system. An end-to-end deep learning process, transfer learning technique, utilizing multiple pre-trained models, combining with class-weighted and focal loss were applied for the classification process. The result was that our ensemble of modified ResNet50 models can classify skin lesions into one of the seven classes with top-1, top-2 and top-3 accuracy 93%, 97% and 99%, respectively. This deep learning system can potentially be integrated into computer-aided diagnosis systems that support dermatologists in skin cancer diagnosis.
