Munjal Geetika, Bhardwaj Paarth, Bhargava Vaibhav, Singh Shivendra, Nagpal Nimish
Amity School of Engineering and Technology Amity University Noida Noida Uttar Pradesh India.
Health Care Sci. 2024 Nov 28;3(6):438-455. doi: 10.1002/hcs2.121. eCollection 2024 Dec.
Skin cancer poses a significant global health threat, with early detection being essential for successful treatment. While deep learning algorithms have greatly enhanced the categorization of skin lesions, the black-box nature of many models limits interpretability, posing challenges for dermatologists.
To address these limitations, SkinSage XAI utilizes advanced explainable artificial intelligence (XAI) techniques for skin lesion categorization. A data set of around 50,000 images from the Customized HAM10000, selected for diversity, serves as the foundation. The Inception v3 model is used for classification, supported by gradient-weighted class activation mapping and local interpretable model-agnostic explanations algorithms, which provide clear visual explanations for model outputs.
SkinSage XAI demonstrated high performance, accurately categorizing seven types of skin lesions-dermatofibroma, benign keratosis, melanocytic nevus, vascular lesion, actinic keratosis, basal cell carcinoma, and melanoma. It achieved an accuracy of 96%, with precision at 96.42%, recall at 96.28%, score at 96.14%, and an area under the curve of 99.83%.
SkinSage XAI represents a significant advancement in dermatology and artificial intelligence by bridging gaps in accuracy and explainability. The system provides transparent, accurate diagnoses, improving decision-making for dermatologists and potentially enhancing patient outcomes.
皮肤癌对全球健康构成重大威胁，早期检测对于成功治疗至关重要。虽然深度学习算法极大地提高了皮肤病变的分类能力，但许多模型的黑箱性质限制了可解释性，给皮肤科医生带来了挑战。
为解决这些局限性，SkinSage XAI利用先进的可解释人工智能（XAI）技术进行皮肤病变分类。一个从定制的HAM10000中选取的约50000张图像的数据集，因其多样性而被选作基础。Inception v3模型用于分类，并由梯度加权类激活映射和局部可解释模型无关解释算法提供支持，这些算法为模型输出提供清晰的视觉解释。
SkinSage XAI表现出高性能，能准确对七种皮肤病变进行分类，即皮肤纤维瘤、良性角化病、黑素细胞痣、血管病变、光化性角化病、基底细胞癌和黑色素瘤。其准确率达到96%，精确率为96.42%，召回率为96.28%，F1分数为96.14%，曲线下面积为99.83%。
SkinSage XAI通过弥合准确性和可解释性方面的差距，代表了皮肤病学和人工智能领域的一项重大进展。该系统提供透明、准确的诊断，改善皮肤科医生的决策，并可能提高患者的治疗效果。
