Template-Type: ReDIF-Article 1.0
Author-Name:Laiba Sohail, Neha Amjad, Tanzeela Asghar, Saria Safdar, Irum Matloob
Author-Email:sariasafdar@fjwu.edu.pk, irum.matloob@fjwu.edu.pk
Author-Workplace-Name:Department of Software Engineering, Fatima Jinnah Women's University, Pakistan
Title:Development of a Machine Learning-Based Predictive System For Classifying Psoriasis
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.
Keywords:Psoriasis, Convolutional Neural Networks (CNN), MobileNetV2
Journal:International Journal of Innovations in Science and Technology
Pages:1022-1038
Volume:7
Issue:2
Year:2025
Month:May
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1407/1916
File-Format: Application/pdf
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1407
File-Format: text/html
Handle: RePEc:abq:IJIST:v:7:y:2025:i:2:p:1022-1038