Splitting the ISIC 2019 data sets of skin lesion.

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，若未在早期阶段检出，可导致患者死亡。多数早期皮肤病变的特征具有较高相似性。用于对多种皮肤病变进行分类的人工智能技术，可有效辅助皮肤科医生挽救患者生命。
本研究提出一种全新方法，充分利用卷积神经网络（Convolutional Neural Network, CNN）混合系统的优势，结合随机森林（Random Forest, RF）与前馈神经网络（Feed Forward Neural Network, FFNN）从皮肤镜图像中提取精细特征，进而构建出对各类皮肤病变具备优异早期检测能力的混合系统。通过融合多组CNN特征，所提方法旨在提升该人工智能系统的鲁棒性与判别能力。
本研究针对ISIC2019数据集对皮肤镜图像进行了优化处理；随后通过梯度向量流（Gradient Vector Flow, GVF）算法，将病变区域从图像背景中分割并分离出来。
第一种用于皮肤镜图像分析以实现皮肤病变早期诊断的策略，是基于CNN-RF与CNN-FFNN混合模型。卷积神经网络模型（包括DenseNet121、MobileNet与VGG19）将输入感兴趣区域（即皮肤病变区域），并为每一处病变生成极具代表性的特征图。第二种分析皮肤病变区域并诊断其类型的策略，是基于融合多卷积神经网络模型特征的CNN-RF与CNN-FFNN混合模型。
基于融合CNN特征的混合模型，在ISIC2019数据集的皮肤镜图像诊断任务中取得了优异结果，可有效区分皮肤癌与其他皮肤病变。
DenseNet121-MobileNet-RF混合模型取得了95.7%的受试者工作特征曲线下面积（Area Under Curve, AUC）、97.7%的准确率、93.65%的精确率、91.93%的灵敏度与99.49%的特异度。