一种用于皮肤癌分类的可解释深度学习方法
An Interpretable Deep Learning Approach for Skin Cancer Categorization
- Ahsanullah University of Science & Technology（阿赫桑拉科技大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究针对皮肤癌早期精准检测需求，采用四种预训练深度学习模型结合图像增强技术进行病变分类，引入XAI提升可解释性，最终XceptionNet以88.72%准确率表现最优，为医学影像辅助诊断提供支撑。
AI中文摘要：
皮肤癌是一项严峻的全球性健康问题，精准的早期检测对于改善患者预后、实现有效治疗至关重要。本研究采用现代深度学习方法与可解释人工智能（explainable artificial intelligence, XAI）技术，解决皮肤癌检测问题。我们使用四种前沿预训练模型对皮肤病变进行分类，分别为XceptionNet、EfficientNetV2S、InceptionResNetV2和EfficientNetV2M。研究采用图像增强方法缓解类别不平衡问题，提升模型的泛化能力。通过引入可解释人工智能（XAI），模型的决策过程可被阐释。在医学领域，可解释性对于建立可信度、推动AI驱动的诊断技术融入临床工作流程至关重要。研究确定XceptionNet架构为性能最优的模型，准确率达到88.72%。本研究展示了深度学习与可解释人工智能（XAI）如何改善皮肤癌诊断，为医学图像分析的未来发展奠定基础。这些技术能够实现早期精准检测，有望提升患者护理质量、降低医疗成本、提高皮肤癌患者的生存率。源代码地址：https://github.com/Faysal-MD/An-Interpretable-Deep-Learning?Approach-for-Skin-Cancer-Categorization-IEEE2023
英文摘要：
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
