Publications

Abstract : 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.