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: https://github.com/Faysal-MD/An-Interpretable-Deep-Learning?Approach-for-Skin-Cancer-Categorization-IEEE2023

翻译：皮肤癌是一项严重的全球性健康问题，早期精准检测对于改善患者预后和实现有效治疗至关重要。本研究采用现代深度学习方法和可解释人工智能（XAI）技术来应对皮肤癌检测的挑战。我们使用四种先进的预训练模型对皮肤病变进行分类：XceptionNet、EfficientNetV2S、InceptionResNetV2和EfficientNetV2M。通过图像增强技术减少类别不平衡问题并提升模型的泛化能力。得益于可解释人工智能（XAI）的实施，我们模型的决策过程得以清晰阐明。在医疗领域，可解释性对于建立可信度以及推动人工智能驱动的诊断技术融入临床工作流程至关重要。我们确定XceptionNet架构为性能最优模型，其准确率达到88.72%。本研究表明深度学习和可解释人工智能（XAI）能够显著提升皮肤癌诊断水平，为医学图像分析领域的未来发展奠定基础。这些技术实现早期精准检测的能力，有望改善患者护理、降低医疗成本，并提高皮肤癌患者的生存率。源代码：https://github.com/Faysal-MD/An-Interpretable-Deep-Learning?Approach-for-Skin-Cancer-Categorization-IEEE2023