Avcı İsmail Anıl, Zirekgür Merve, Karakaya Barış, Demir Betül
Department of Electrical-Electronics Engineering, Faculty of Technology, Firat University, 23200 Elazig, Turkey.
Department of Electrical and Electronics Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, 44200 Malatya, Turkey.
Diagnostics (Basel). 2024 Dec 28;15(1):55. doi: 10.3390/diagnostics15010055.
Psoriasis is a chronic, immune-mediated skin disease characterized by lifelong persistence and fluctuating symptoms. The clinical similarities among its subtypes and the diversity of symptoms present challenges in diagnosis. Early diagnosis plays a vital role in preventing the spread of lesions and improving patients' quality of life. This study proposes a hybrid model combining multiple transfer learning and ensemble learning methods to classify psoriasis subtypes accurately and efficiently. The dataset includes 930 images labeled by expert dermatologists from the Dermatology Clinic of Fırat University Hospital, representing four distinct subtypes: generalized, guttate, plaque, and pustular. Class imbalance was addressed by applying synthetic data augmentation techniques, particularly for the rare subtype. To reduce the influence of nonlesion environmental factors, the images underwent systematic cropping and preprocessing steps, such as Gaussian blur, thresholding, morphological operations, and contour detection. DenseNet-121, EfficientNet-B0, and ResNet-50 transfer learning models were utilized to extract feature vectors, which were then combined to form a unified feature set representing the strengths of each model. The feature set was divided into 80% training and 20% testing subsets and evaluated using a hard voting classifier consisting of logistic regression, random forest, support vector classifier, k-nearest neighbors, and gradient boosting algorithms. The proposed hybrid approach achieved 93.14% accuracy, 96.75% precision, and an F1 score of 91.44%, demonstrating superior performance compared to individual transfer learning models. This method offers significant potential to enhance the classification of psoriasis subtypes in clinical and real-world settings.
银屑病是一种慢性、免疫介导的皮肤病，其特征是症状会终身持续且波动变化。其各亚型之间的临床相似性以及症状的多样性给诊断带来了挑战。早期诊断对于防止皮损扩散和提高患者生活质量起着至关重要的作用。本研究提出了一种结合多种迁移学习和集成学习方法的混合模型，以准确、高效地对银屑病亚型进行分类。该数据集包括来自菲拉特大学医院皮肤科诊所的专家皮肤科医生标注的930张图像，代表四种不同的亚型：泛发性、点滴状、斑块状和脓疱状。通过应用合成数据增强技术来解决类别不平衡问题，特别是针对罕见亚型。为了减少非皮损环境因素的影响，对图像进行了系统裁剪以及高斯模糊、阈值处理、形态学操作和轮廓检测等预处理步骤。利用DenseNet - 121、EfficientNet - B0和ResNet - 50迁移学习模型提取特征向量，然后将这些特征向量组合形成一个统一的特征集，该特征集代表了每个模型的优势。将特征集分为80%的训练子集和20%的测试子集，并使用由逻辑回归、随机森林、支持向量分类器、k近邻和梯度提升算法组成的硬投票分类器进行评估。所提出的混合方法实现了93.14%的准确率、96.75%的精确率和91.44%的F1分数，与单个迁移学习模型相比表现出卓越的性能。该方法在临床和实际应用中对增强银屑病亚型分类具有巨大潜力。
