Computer Department, Applied College, Najran University, Najran, Saudi Arabia.
Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana'a, Yemen.
PLoS One. 2024 Mar 21;19(3):e0298305. doi: 10.1371/journal.pone.0298305. eCollection 2024.
Skin cancer is one of the most fatal skin lesions, capable of leading to fatality if not detected in its early stages. The characteristics of skin lesions are similar in many of the early stages of skin lesions. The AI in categorizing diverse types of skin lesions significantly contributes to and helps dermatologists to preserve patients' lives. This study introduces a novel approach that capitalizes on the strengths of hybrid systems of Convolutional Neural Network (CNN) models to extract intricate features from dermoscopy images with Random Forest (Rf) and Feed Forward Neural Networks (FFNN) networks, leading to the development of hybrid systems that have superior capabilities early detection of all types of skin lesions. By integrating multiple CNN features, the proposed methods aim to improve the robustness and discriminatory capabilities of the AI system. The dermoscopy images were optimized for the ISIC2019 dataset. Then, the area of the lesions was segmented and isolated from the rest of the image by a Gradient Vector Flow (GVF) algorithm. The first strategy for dermoscopy image analysis for early diagnosis of skin lesions is by the CNN-RF and CNN-FFNN hybrid models. CNN models (DenseNet121, MobileNet, and VGG19) receive a region of interest (skin lesions) and produce highly representative feature maps for each lesion. The second strategy to analyze the area of skin lesions and diagnose their type by means of CNN-RF and CNN-FFNN hybrid models based on the features of the combined CNN models. Hybrid models based on combined CNN features have achieved promising results for diagnosing dermoscopy images of the ISIC 2019 dataset and distinguishing skin cancers from other skin lesions. The Dense-Net121-MobileNet-RF hybrid model achieved an AUC of 95.7%, an accuracy of 97.7%, a precision of 93.65%, a sensitivity of 91.93%, and a specificity of 99.49%.
皮肤癌是最致命的皮肤病变之一，如果在早期阶段没有发现，就有可能导致死亡。皮肤病变的特征在皮肤病变的早期阶段很多都是相似的。人工智能在对不同类型的皮肤病变进行分类方面做出了重大贡献，并帮助皮肤科医生挽救患者的生命。本研究介绍了一种新方法，该方法利用卷积神经网络（CNN）模型的混合系统的优势，从皮肤镜图像中提取复杂特征，结合随机森林（Rf）和前馈神经网络（FFNN）网络，开发出具有优越能力的混合系统，可以早期检测所有类型的皮肤病变。通过整合多个 CNN 特征，提出的方法旨在提高 AI 系统的稳健性和辨别能力。对 ISIC2019 数据集进行了皮肤镜图像优化。然后，通过梯度矢量流（GVF）算法将病变区域从其余图像中分割并分离出来。用于皮肤病变早期诊断的皮肤镜图像分析的第一种策略是使用 CNN-RF 和 CNN-FFNN 混合模型。CNN 模型（DenseNet121、MobileNet 和 VGG19）接收感兴趣区域（皮肤病变），并为每个病变生成高度代表性的特征图。第二种策略是通过 CNN-RF 和 CNN-FFNN 混合模型基于组合 CNN 模型的特征来分析皮肤病变区域并诊断其类型。基于组合 CNN 特征的混合模型在诊断 ISIC 2019 数据集的皮肤镜图像和区分皮肤癌与其他皮肤病变方面取得了有希望的结果。Dense-Net121-MobileNet-RF 混合模型的 AUC 为 95.7%，准确率为 97.7%，精度为 93.65%，敏感性为 91.93%，特异性为 99.49%。
