Mohammed Ahmad Salahaddin, Mohammed Ali Maryam Sarmad, Mohammed Jihad Abdalwahid Shadan, Wahhab Kareem Shahab
College of Engineering and Computer Science, Department of Computer Network, Lebanese French University, Kurdistan Region, Iraq.
Department of Technical Information Systems Engineering, Technical Engineering College, Erbil Polytechnic University, KRG, Iraq.
Asian Pac J Cancer Prev. 2025 Jul 1;26(7):2607-2617. doi: 10.31557/APJCP.2025.26.7.2607.
Melanoma is one of the most dreaded types of cancer in the world today, and therefore, early detection becomes crucial. Deep learning models need annotated training data, and such training data are difficult and expensive to acquire. To solve this challenge, we introduce a new framework called the Contrastive Self-Supervised Ensemble Transfer Learning (CSSL-ETL) that combines the techniques of CSSL and ETL to improve the feature learning and classification accuracy of the model.
The CSSL-ETL framework integrates Contrastive Self-Supervised Learning (CSSL) and Ensemble Transfer Learning (ETL) techniques. Utilises CSSL, which pre-trains models on a vast array of images and enhances the generalization of models to unlabeled skin images, whereas ETL captures various Feature extraction power evolves the ConvNeXt-Large, Swin Transformer V2, and EfficientNetV2 models.
Using the same metrics on both datasets, ISIC and HAM10000, such accuracies are 94.6%, precisions of 93.8% and recalls of 91.5%, as well as the AUC-ROC of 96.1%, which is higher than the ResNet-50, EfficientNetV2, and Swin Transformer feeds forward neural network models. The assessment of the confusion matrix also reveals low misclassifications, particularly in the ability to identify melanoma. Coping with clinical and thermoscopic images in a combined manner increases the diagnostic capabilities of the system. On the same note, federated learning takes into consideration the private architecture of the model across institutions in the context of AI. The incorporation of Grad-CAM++ and the Bayesian estimate of uncertainty enhances the models' transparency and, ultimately, the clinicians' confidence in those models.
Compared to the previous methods, CSSL-ETL represents the features better and strengthens the classification ability and generalization ability. As for future work, real-time m-health applications as well as data fusion using multiple sources of data, which will enhance the automation of skin cancer detection, will be the next areas of concern.
黑色素瘤是当今世界最可怕的癌症类型之一，因此早期检测至关重要。深度学习模型需要带注释的训练数据，而获取此类训练数据既困难又昂贵。为应对这一挑战，我们引入了一种名为对比自监督集成迁移学习（CSSL-ETL）的新框架，该框架结合了CSSL和ETL技术，以提高模型的特征学习和分类准确率。
CSSL-ETL框架集成了对比自监督学习（CSSL）和集成迁移学习（ETL）技术。利用CSSL在大量图像上对模型进行预训练，并增强模型对未标记皮肤图像的泛化能力，而ETL捕获各种特征提取能力，改进了ConvNeXt-Large、Swin Transformer V2和EfficientNetV2模型。
在ISIC和HAM10000这两个数据集上使用相同的指标，准确率为94.6%，精确率为93.8%，召回率为91.5%，AUC-ROC为96.1%，高于ResNet-50、EfficientNetV2和Swin Transformer前馈神经网络模型。混淆矩阵评估还显示错误分类率较低，尤其是在识别黑色素瘤的能力方面。以组合方式处理临床和热成像图像可提高系统的诊断能力。同样，联邦学习在人工智能背景下考虑了跨机构模型的私有架构。Grad-CAM++的纳入和不确定性的贝叶斯估计提高了模型的透明度，并最终增强了临床医生对这些模型的信心。
与先前方法相比，CSSL-ETL能更好地表示特征，增强了分类能力和泛化能力。至于未来的工作，实时移动健康应用以及使用多源数据的数据融合将是接下来关注的领域，这将提高皮肤癌检测的自动化程度。
