Skin cancer is a serious worldwide health issue, precise and early detection
is essential for better patient outcomes and effective treatment. In this
research, we use modern deep learning methods and explainable artificial
intelligence (XAI) approaches to address the problem of skin cancer detection.
To categorize skin lesions, we employ four cutting-edge pre-trained models:
XceptionNet, EfficientNetV2S, InceptionResNetV2, and EfficientNetV2M. Image
augmentation approaches are used to reduce class imbalance and improve the
generalization capabilities of our models. Our models decision-making process
can be clarified because of the implementation of explainable artificial
intelligence (XAI). In the medical field, interpretability is essential to
establish credibility and make it easier to implement AI driven diagnostic
technologies into clinical workflows. We determined the XceptionNet
architecture to be the best performing model, achieving an accuracy of 88.72%.
Our study shows how deep learning and explainable artificial intelligence (XAI)
can improve skin cancer diagnosis, laying the groundwork for future
developments in medical image analysis. These technologies ability to allow for
early and accurate detection could enhance patient care, lower healthcare
costs, and raise the survival rates for those with skin cancer. Source Code:
this https URL

使用现代深度学习方法和可解释人工智能（XAI）来解决皮肤癌检测问题，采用四个先进的预训练模型进行皮肤病变分类，XceptionNet 被确定为表现最佳的模型，本研究展示了深度学习和可解释人工智能（XAI）如何改善皮肤癌诊断，为医学图像分析领域未来的发展奠定了基础。