Sarı Merve Okumuş, Keser Kübra
Simav Faculty of Technology, Department of Electrical and Electronics Engineering, Kutahya Dumlupinar University, Simav, 43500, Kutahya, Turkey.
Sci Rep. 2025 Jul 28;15(1):27506. doi: 10.1038/s41598-025-13275-x.
Skin diseases are one of the most common health problems that affect people of all ages around the world and significantly reduce the quality of life of individuals. Diseases of eczema, seborrheic dermatitis and skin cancer need to be diagnosed and correctly classified promptly. This issue, which is of great importance in terms of control and practical and effective treatment, is the study's starting point. The study included 693 individuals with eczema, 750 with skin cancer and 770 with seborrheic dermatitis. In the study, which focused on the classification of 3 different skin diseases, the Relief algorithm was used to increase the classification success and to ensure the selection of more meaningful qualities. With AlexNet with cross-validation, the accuracy rate was 89.39% for 80% training and 20% test rates. When SVM classification with the Relief algorithm was used for the same rates, the accuracy rate was 92.10%. In the analysis performed on the ISIC 2017 dataset, the accuracy rate is 89.16% for 80% training and 20% test rate. When the training and test rate was changed to 70% training and 30% test rate, the accuracy rate was 91.11%. It was observed that SVM classification with Relief's algorithm offers higher accuracy rates than other methods. The proposed model provides an original contribution to the literature, particularly through its integration of feature selection and a simplified architecture. This high success rate reveals that deep learning is an effective method in classifying skin diseases and the transfer learning process and will reduce the mortality rates due to cancer diseases with early and effective treatment while enabling skin diseases to be easily distinguished.
皮肤疾病是影响全球所有年龄段人群的最常见健康问题之一，会显著降低个人生活质量。湿疹、脂溢性皮炎和皮肤癌等疾病需要及时诊断并正确分类。这个在控制以及实际有效治疗方面非常重要的问题，是该研究的出发点。该研究纳入了693名湿疹患者、750名皮肤癌患者和770名脂溢性皮炎患者。在这项聚焦于三种不同皮肤疾病分类的研究中，使用了Relief算法来提高分类成功率并确保选择更有意义的特征。通过带有交叉验证的AlexNet，在80%训练率和20%测试率的情况下，准确率为89.39%。当对相同比率使用带有Relief算法的支持向量机分类时，准确率为92.10%。在对ISIC 2017数据集进行的分析中，80%训练率和20%测试率时的准确率为89.16%。当训练和测试率变为70%训练率和30%测试率时，准确率为91.11%。观察到带有Relief算法的支持向量机分类比其他方法提供更高的准确率。所提出的模型为文献做出了原创性贡献，特别是通过其特征选择和简化架构的整合。这种高成功率表明深度学习是一种对皮肤疾病进行分类以及迁移学习过程的有效方法，并且通过早期有效治疗将降低癌症疾病的死亡率，同时使皮肤疾病易于区分。
