皮肤癌,包括高度致命的恶性黑色素瘤,随着发病率的上升,对全球健康构成了重大挑战。早期检测在提高生存率方面起着关键作用。这项研究旨在开发一种先进的基于深度学习的方法,用于准确的皮肤病变分类,解决数据可用性有限等挑战,阶级不平衡,和噪音。现代深度神经网络设计,例如ResNeXt101、SeResNeXt101、ResNet152V2、DenseNet201、GoogLeNet、和Xception,它们在研究中使用,并使用SGD技术进行了优化。该数据集包括来自HAM10000和ISIC数据集的各种皮肤病变图像。噪音和伪影使用图像修复来解决,数据增强技术增强了训练样本的多样性。利用集成技术,创建平均和加权平均集成模型。网格搜索优化模型权重分布各个模型表现出不同的性能,包括召回在内的指标,精度,F1得分,和MCC。“平均集合模型”实现了和谐平衡,强调精度,F1得分,和回忆,产生高性能。“加权集合模型”利用了各个模型的优势,展示提高的精度和MCC,取得了出色的表现。合奏模型始终优于单个模型,平均集成模型的宏观平均ROC-AUC评分为96%,加权集成模型的宏观平均ROC-AUC评分为97%。这项研究证明了集成技术在显着提高皮肤病变分类准确性方面的功效。通过利用各个模型的优势并解决其局限性,集成模型在各种指标上表现出稳健和可靠的性能。这些发现强调了集成技术在增强医学诊断和改善患者皮肤病变诊断方面的潜力。
Skin cancer, including the highly lethal malignant melanoma, poses a significant global health challenge with a rising incidence rate. Early detection plays a pivotal role in improving survival rates. This study aims to develop an advanced deep learning-based approach for accurate skin lesion classification, addressing challenges such as limited data availability, class imbalance, and noise. Modern deep neural network designs, such as ResNeXt101, SeResNeXt101, ResNet152V2, DenseNet201, GoogLeNet, and Xception, which are used in the study and ze optimised using the SGD technique. The dataset comprises diverse skin lesion images from the HAM10000 and ISIC datasets. Noise and artifacts are tackled using image inpainting, and data augmentation techniques enhance training sample diversity. The ensemble technique is utilized, creating both average and weighted average ensemble models. Grid search optimizes model weight distribution. The individual models exhibit varying performance, with metrics including recall, precision, F1 score, and MCC. The \"Average ensemble model\" achieves harmonious balance, emphasizing precision, F1 score, and recall, yielding high performance. The \"Weighted ensemble model\" capitalizes on individual models\' strengths, showcasing heightened precision and MCC, yielding outstanding performance. The ensemble models consistently outperform individual models, with the average ensemble model attaining a macro-average ROC-AUC score of 96 % and the weighted ensemble model achieving a macro-average ROC-AUC score of 97 %. This research demonstrates the efficacy of ensemble techniques in significantly improving skin lesion classification accuracy. By harnessing the strengths of individual models and addressing their limitations, the ensemble models exhibit robust and reliable performance across various metrics. The findings underscore the potential of ensemble techniques in enhancing medical diagnostics and contributing to improved patient outcomes in skin lesion diagnosis.