Development of a Machine Learning-Based Predictive System For Classifying Psoriasis
Journal Title: International Journal of Innovations in Science and Technology - Year 2025, Vol 7, Issue 2
Abstract
Psoriasis is a chronic autoimmune skin condition characterized by inflamed, flaky patches that affect both physical consolation and passionate well-being. Opportune and exact determination is basic for viable treatment; however, it remains troublesome due to its likeness to other dermatological disorders. This research presents a Psoriasis Detection and Severity Classification Framework built on MobileNetV2, a lightweight and effective profound learning demonstration custom-fitted for real-time utilization in resource-constrained situations. Through a basic image-upload interface, healthcare suppliers or patients can yield scalp pictures for robotized investigation. The framework to begin with recognizes the nearness of psoriasis with 90% accuracy and, at that point classifies its seriousness as either “low” or “moderate to severe” with 87% accuracy. This two-step preparation conveys prompt and clinically profitable experiences, supporting more focused and opportune care. Approved in a clinical setting, the demonstration illustrates solid unwavering quality and down-to-earth appropriateness. It decreases reliance on expert-driven diagnostics and quickens treatment choices. By coordinating AI with restorative hone, this framework improves demonstrative accuracy,streamlines workflows, and engages clinicians to convey speedier, more personalized care reshaping the scene of dermatological healthcare.
Authors and Affiliations
Laiba Sohail, Neha Amjad, Tanzeela Asghar, Saria Safdar, Irum Matloob
Keywords
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