The design of an efficient computer-aided diagnosis (CADx) system for psoriasis severity assessment demands both accurate segmentation and classification of psoriasis lesions. Recently, few studies have been conducted to design automatic CADx systems for psoriasis severity assessment using traditional machine learning approaches. However, these approaches are highly featured dependent and require extensive and careful feature extraction. Among a large number of features extracted, assessing the features which contribute significantly to the classifier performing is a difficult and time-consuming task. Large features lead to poor generalization, due to high inter and intra-class variation of psoriasis skin lesions. This makes the task of implementing a reliable CADx system challenging. In such similar cases, Deep learning-based approaches have been proven better because of their ability to learn and make intelligent decisions automatically. In this study, a fully automated deep learning-based CADx system for psoriasis has been proposed. The system combines three modules in a single framework for achieving different objectives namely; recognition of psoriasis and non-psoriasis disease, automatic segmentation of psoriatic lesion, and its severity assessment. The modified U-Net and modified VGG-16 model have been implemented and trained for the segmentation and classification task respectively. The severity assessment module is capable of extracting discriminative features specifically related to the psoriatic lesion, which is automatically segmented by the segmentation module. The performance of the proposed CADx framework has been extensively evaluated on an extensive psoriasis dataset using k-fold cross-validation procedure. The appropriateness of the proposed system has been justified in terms of its performance at each of the three stages along with benchmarking against previously reported systems. Further, the system accuracy and reliability index has been evaluated for a dataset of varying size to validate the consistency of the proposed system.

用于牛皮癣严重程度评估的有效计算机辅助诊断（CADx）系统的设计需要对牛皮癣病变进行准确的分割和分类。最近，很少有研究使用传统的机器学习方法来设计用于牛皮癣严重性评估的自动CADx系统。但是，这些方法高度依赖特征，并且需要大量且仔细的特征提取。在提取的大量特征中，评估对分类器执行有重大贡献的特征是一项困难且耗时的任务。由于牛皮癣皮肤病变的类间和类内差异较大，较大的特征导致泛化性差。这使得实施可靠的CADx系统的任务变得困难。在类似的情况下，基于深度学习的方法已被证明是更好的方法，因为它们能够自动学习和做出明智的决策。在这项研究中，已经提出了一种基于全自动深度学习的牛皮癣CADx系统。该系统将三个模块组合在一个框架中，以实现不同的目标。牛皮癣和非牛皮癣疾病的认识，牛皮癣病变的自动分割及其严重性评估。修改后的U-Net模型和修改后的VGG-16模型已分别实施和训练以进行细分和分类任务。严重性评估模块能够提取与银屑病病变特别相关的判别特征，并由分割模块自动对其进行分割。k倍交叉验证过程。就三个阶段中每个阶段的性能以及与先前报告的系统的基准测试而言，所提出系统的适当性已得到证明。此外，已针对大小可变的数据集评估了系统准确性和可靠性指标，以验证所提出系统的一致性。