Abdulredah Ali Atshan, Fadhel Mohammed A, Alzubaidi Laith, Duan Ye, Kherallah Monji, Charfi Faiza
National School of Electronics and Telecoms of Sfax, University of Sfax, Sfax, Tunisia.
College of Computer Science and Information Technology, University of Sumer, Thi-Qar, Iraq.
BMC Med Inform Decis Mak. 2025 Jan 31;25(1):48. doi: 10.1186/s12911-025-02889-w.
This paper introduces SkinWiseNet (SWNet), a deep convolutional neural network designed for the detection and automatic classification of potentially malignant skin cancer conditions. SWNet optimizes feature extraction through multiple pathways, emphasizing network width augmentation to enhance efficiency. The proposed model addresses potential biases associated with skin conditions, particularly in individuals with darker skin tones or excessive hair, by incorporating feature fusion to assimilate insights from diverse datasets. Extensive experiments were conducted using publicly accessible datasets to evaluate SWNet's effectiveness.This study utilized four datasets-Mnist-HAM10000, ISIC2019, ISIC2020, and Melanoma Skin Cancer-comprising skin cancer images categorized into benign and malignant classes. Explainable Artificial Intelligence (XAI) techniques, specifically Grad-CAM, were employed to enhance the interpretability of the model's decisions. Comparative analysis was performed with three pre-existing deep learning networks-EfficientNet, MobileNet, and Darknet. The results demonstrate SWNet's superiority, achieving an accuracy of 99.86% and an F1 score of 99.95%, underscoring its efficacy in gradient propagation and feature capture across various levels. This research highlights the significant potential of SWNet in advancing skin cancer detection and classification, providing a robust tool for accurate and early diagnosis. The integration of feature fusion enhances accuracy and mitigates biases associated with hair and skin tones. The outcomes of this study contribute to improved patient outcomes and healthcare practices, showcasing SWNet's exceptional capabilities in skin cancer detection and classification.
本文介绍了SkinWiseNet（SWNet），这是一种深度卷积神经网络，旨在检测潜在的恶性皮肤癌状况并进行自动分类。SWNet通过多种途径优化特征提取，强调增加网络宽度以提高效率。所提出的模型通过纳入特征融合以吸收来自不同数据集的见解，解决了与皮肤状况相关的潜在偏差，特别是在肤色较深或毛发过多的个体中。使用可公开获取的数据集进行了广泛实验，以评估SWNet的有效性。本研究使用了四个数据集——Mnist-HAM10000、ISIC2019、ISIC2020和黑色素瘤皮肤癌，这些数据集包含分类为良性和恶性类别的皮肤癌图像。采用了可解释人工智能（XAI）技术，特别是Grad-CAM，以增强模型决策的可解释性。与三个现有的深度学习网络——EfficientNet、MobileNet和Darknet进行了对比分析。结果表明SWNet具有优越性，准确率达到99.86%，F1分数达到99.95%，突出了其在不同层次上梯度传播和特征捕获方面的有效性。这项研究突出了SWNet在推进皮肤癌检测和分类方面的巨大潜力，为准确和早期诊断提供了一个强大的工具。特征融合的整合提高了准确性，并减轻了与毛发和肤色相关的偏差。本研究的结果有助于改善患者预后和医疗实践，展示了SWNet在皮肤癌检测和分类方面的卓越能力。
