PsLSNetV2: End to end deep learning system for measurement of area score of psoriasis regions in color images. (January 2023) - Raj, Ritesh; Londhe, Narendra D.; Sonawane, Rajendra
Highlights: Proposed a deep learning-based fully automated and single-stage framework that helps in objective assessment of psoriasis area score from color images of different body regions of psoriasis patients. Proposed model performs the multi-class segmentation of image into three different regions namely healthy skin, psoriasis lesion and background regions simultaneously. Proposed model utilizes an efficient and lightweight network for transfer learning to improve the feature representational efficiency. Segmentation performance of the proposed model is evaluated by using five different performance metrics and validated by using a fivefold cross-validation technique. Proposed methodology is extensively compared with other deep learning-based segmentation models and existing literature to validate the promising performance of the proposed methodology. Abstract: Objective: At present, psoriasis area scores are measured manually by dermatologists through visual observations. This subjective method suffers from numerous typical problems. The only solution to these problems is to design and implement objective methods for this. However, most of the existing works in this regard are based on machine learning frameworks that are semi-automated and feature-dependent. In this work, a deep learning-based fully automated, and single-stage framework is proposed to detect psoriasis lesions and measure their area score from color images of human body regions. Methods: The proposed method is an extension of the existing PsLSNet proposed by our team, which provides a fully automated approach for the segmentation of single psoriasis lesions from cropped patches of skin images. For this proposed work, a new version PsLSNetV2 model is developed for automated segmentation of healthy skin, multiple psoriasis lesions, and background region simultaneously in complete body region images. This proposed model utilizes an efficient and lightweight network with transfer learning to increase the representational efficiency for multi-class segmentation. Results: The proposed model is tested by 5-fold cross-validation on a self-generated dataset having 500 images from 100 psoriasis patients. The multi-class segmentation performance of the proposed model achieves an overall Dice-Coefficient Index and Jaccard Index of 97.43% and 95.05% respectively and outperforms the existing models. Conclusion: The fully automated multi-class segmentation results by the proposed lightweight segmentation model are promising enough to determine psoriasis area score objectively with an average accuracy of 94.20% for assisting dermatologists in a simple and rapid way.