Abstract：
The leading cause of cancer-related deaths worldwide is skin cancer. Effective therapy depends on the early diagnosis of skin cancer through the precise classification of skin lesions. However, dermatologists may find it difficult and time-consuming to accurately classify skin lesions. The use of transfer learning to boost skin cancer classification model precision is a promising strategy. In this work, we proposed a hybrid CNN with a transfer learning model and a random forest classifier for skin cancer disease detection. To evaluate the efficacy of the proposed model, it was verified over two datasets of benign skin moles and malignant skin moles. The proposed model is able to classify images with an accuracy of up to 90.11%. The empirical results and analysis assure the feasibility and effectiveness of the proposed model for skin cancer classification.
摘要：
全球癌症相关死亡的主要原因是皮肤癌。有效的治疗取决于通过皮肤病变的精确分类对皮肤癌的早期诊断。然而,皮肤科医生可能会发现对皮肤病变进行准确分类既困难又耗时。使用迁移学习来提高皮肤癌分类模型的精度是一种有前途的策略。在这项工作中,我们提出了一种具有迁移学习模型和随机森林分类器的混合CNN，用于皮肤癌疾病检测。为了评估所提出模型的有效性，在良性皮肤痣和恶性皮肤痣的两个数据集上进行了验证。所提出的模型能够对图像进行分类，准确率高达90.11％。实证结果和分析保证了所提出的皮肤癌分类模型的可行性和有效性。
