This paper is the first work of its kind to exploit the power of the visual recognition techniques in the area of psoriasis. We train machine learning models on five different erythema severity classes.
Book Title: Psoriasis Image Representation Using Patch-based Dictionary Learning for Erythema Severity Scoring
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Published on 2018 by
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Book is About: Psoriasis
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Highlights: We propose a fully automated method for erythema severity scoring using unsupervised feature learning techniques. The used psoriasis images set is considered the largest set to-date that has been used in scoring psoriasis severity. This paper is the first work of its kind to exploit the power of the visual recognition techniques in the area of psoriasis. We train machine learning models on five different erythema severity classes. Abstract: Psoriasis is a chronic skin disease which can be life-threatening. Accurate severity scoring helps dermatologists to decide on the treatment. In this paper, we present a semi-supervised computer-aided system for automatic erythema severity scoring in psoriasis images. Firstly, the unsupervised stage includes a novel image representation method. We construct a dictionary, which is then used in the sparse representation for local feature extraction. To acquire the final image representation vector, an aggregation method is exploited over the local features. Secondly, the supervised phase is where various multi-class machine learning (ML) classifiers are trained for erythema severity scoring. Finally, we compare the proposed system with two popular unsupervised feature extractor methods, namely: bag of visual words model (BoVWs) and AlexNet pretrained model. Root mean square error (RMSE) and F1 score are used as performance measures for the learned dictionaries and the trained ML models, respectively. A psoriasis image set consisting of 676 images, is used in this study. Experimental results demonstrate that the use of the proposed procedure can provide a setup where erythema scoring is accurate and consistent. Also, it is revealed that dictionaries with large number of atoms and small patch sizes yield the best representative erythema severity features. Further, random forest (RF) outperforms other classifiers with F1 score 0.71, followed by support vector machine (SVM) and boosting with 0.66 and 0.64 scores, respectively. Furthermore, the conducted comparative studies confirm the effectiveness of the proposed approach with improvement of 9% and 12% over BoVWs and AlexNet based features, respectively.
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