Authors - Gauthaman S P, Paneer Thanu Swaroop C, Bagavathi Sivakumar P, Anantha Narayanan V Abstract - Psoriasis is a long-term inflammatory skin disease commonly identi fied by red plaques, scaling, and abnormal thickening of the epidermis. Reliable evaluation of disease severity is important for determining appropriate treatment options and for tracking patient response to therapy. In clinical practice, severity is often assessed using the Psoriasis Area and Severity Index (PASI). Although widely adopted, this method largely depends on visual examination and clinician judgment, which may lead to inconsistencies and observer-dependent variations. Recent developments in artificial intelligence and non-invasive dermatological imaging technologies provide opportunities for more objective and automated assessment of skin disorders. In this study, a novel framework is proposed for psoriasis severity evaluation that integrates skin biomechanical characteristics with deep hybrid learning mod els. Biomechanical attributes of the skin, including elasticity, stiffness, and vis coelastic behavior, are obtained through non-invasive measurement techniques and combined with visual information derived from dermatological images. The proposed system employs a hybrid deep learning architecture that incorporates convolutional neural networks (CNN) for image feature extraction along with machine learning classifiers for severity prediction. By jointly analyzing biome chanical and visual features, the framework aims to enhance the precision, con sistency, and reproducibility of psoriasis severity assessment. Experimental anal ysis indicates that the inclusion of biomechanical biomarkers alongside deep learning significantly improves prediction performance when compared with tra ditional image-based models. The proposed approach can support dermatologists in clinical decision-making and may also facilitate applications in tele-dermatol ogy and personalized disease monitoring.
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