Development of a Machine Learning-Based Predictive System For Classifying Psoriasis

Laiba Sohail; Neha Amjad; Tanzeela Asghar; Saria Safdar; Irum Matloob

Authors

Laiba Sohail Department of Software Engineering, Fatima Jinnah Women's University, Pakistan
Neha Amjad Department of Software Engineering, Fatima Jinnah Women's University, Pakistan
Tanzeela Asghar Department of Software Engineering, Fatima Jinnah Women's University, Pakistan
Saria Safdar Department of Software Engineering, Fatima Jinnah Women's University, Pakistan
Irum Matloob Department of Software Engineering, Fatima Jinnah Women's University, Pakistan

Keywords:

Psoriasis, Convolutional Neural Networks (CNN), MobileNetV2

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 demonstrate custom fitted for real-time utilize 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, at that point classifies its serious- ness as either “low” or “moderate to severe” with 87% accuracy. This two-step prepare conveys prompt and clinically profitable experiences, supporting more focused on and opportune care. Approved in a clinical setting, the demonstrate illustrates solid unwaver- ing quality and down-to-earth appropriateness. It decreases reliance on expert-driven diagnostics and quickens treatment choices. By coordination AI with restorative hone, this framework improves demonstrative accuracy, streamlines workflows, and engages clini-cians to convey speedier, more personalized care reshaping the scene of dermatological .

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