Department of Software Engineering, Faculty of Informatics Engineering, Kaunas University of Technology, LT-51386 Kaunas, Lithuania.
Neural Netw. 2023 Mar;160:238-258. doi: 10.1016/j.neunet.2023.01.022. Epub 2023 Jan 24.
The idea of smart healthcare has gradually gained attention as a result of the information technology industry's rapid development. Smart healthcare uses next-generation technologies i.e., artificial intelligence (AI) and Internet of Things (IoT), to intelligently transform current medical methods to make them more efficient, dependable and individualized. One of the most prominent uses of telemedicine and e-health in medical image analysis is teledermatology. Telecommunications technologies are used in this industry to send medical information to professionals. Teledermatology is a useful method for the identification of skin lesions, particularly in rural locations, because the skin is visually perceptible. One of the most recent tools for diagnosing skin cancer is dermoscopy. To classify skin malignancies, numerous computational approaches have been proposed in the literature. However, difficulties still exist i.e., lesions with low contrast, imbalanced datasets, high level of memory complexity, and the extraction of redundant features.
In this work, a unified CAD model is proposed based on a deep learning framework for skin lesion segmentation and classification. In the proposed approach, the source dermoscopic images are initially pre-processed using a contrast enhancement based modified bio-inspired multiple exposure fusion approach. In the second stage, a custom 26-layered convolutional neural network (CNN) architecture is designed to segment the skin lesion regions. In the third stage, four pre-trained CNN models (Xception, ResNet-50, ResNet-101 and VGG16) are modified and trained using transfer learning on the segmented lesion images. In the fourth stage, the deep features vectors are extracted from all the CNN models and fused using the convolutional sparse image decomposition fusion approach. In the fifth stage, the univariate measurement and Poisson distribution feature selection approach is used for the best features selection for classification. Finally, the selected features are fed to the multi-class support vector machine (MC-SVM) for the final classification.
The proposed approach employed to the HAM10000, ISIC2018, ISIC2019, and PH2 datasets and achieved an accuracy of 98.57%, 98.62%, 93.47%, and 98.98% respectively which are better than previous works.
When compared to renowned state-of-the-art methods, experimental results show that the proposed skin lesion detection and classification approach achieved higher performance in terms of both visually and enhanced quantitative evaluation with enhanced accuracy.
随着信息技术产业的飞速发展，智慧医疗的理念逐渐受到关注。智慧医疗利用人工智能（AI）和物联网（IoT）等下一代技术，智能地改变当前的医疗方法，使其更高效、可靠和个性化。远程医疗和电子医疗在医学图像分析中最突出的应用之一是远程皮肤病学。该行业使用电信技术将医疗信息发送给专业人员。远程皮肤病学是识别皮肤病变的一种有用方法，特别是在农村地区，因为皮肤是肉眼可见的。诊断皮肤癌的最新工具之一是皮肤镜检查。为了对皮肤恶性肿瘤进行分类，文献中提出了许多计算方法。然而，仍然存在一些困难，例如对比度低的病变、不平衡的数据集、高内存复杂度和冗余特征的提取。
在这项工作中，提出了一种基于深度学习框架的皮肤病变分割和分类的统一 CAD 模型。在提出的方法中，首先使用基于对比度增强的改进生物启发多曝光融合方法对源皮肤镜图像进行预处理。在第二阶段，设计了一个定制的 26 层卷积神经网络（CNN）架构来分割皮肤病变区域。在第三阶段，使用迁移学习对分割的病变图像对四个预先训练的 CNN 模型（Xception、ResNet-50、ResNet-101 和 VGG16）进行修改和训练。在第四阶段，从所有 CNN 模型中提取深度特征向量，并使用卷积稀疏图像分解融合方法进行融合。在第五阶段，使用单变量测量和泊松分布特征选择方法对分类进行最佳特征选择。最后，将选择的特征输入多类支持向量机（MC-SVM）进行最终分类。
该方法应用于 HAM10000、ISIC2018、ISIC2019 和 PH2 数据集，分别获得了 98.57%、98.62%、93.47%和 98.98%的准确率，优于以往的工作。
与著名的最新技术相比，实验结果表明，所提出的皮肤病变检测和分类方法在视觉和增强的定量评估方面都具有更高的性能，并且具有更高的准确性。
