Portable Skin Imaging Light-Box for Clinical Grade Skin Scans

Author

Kayandra Lewis, Kennesaw State UniversityFollow
Cayden Skidmore, Kennesaw State UniversityFollow
Komi Atitsogbe Golo, Kennesaw State UniversityFollow

Date of Submission

Fall 12-9-2025

Project Type

Senior Design

Major

Industrial & Systems Engineering

Degree Name

Industrial & Systems Engineering

Department

Department of Industrial & Systems Engineering

Committee Chair/First Advisor

Dr. Nino

Abstract

This project focuses on designing and developing a modular, portable skin-imaging light box to improve the consistency and accuracy of smartphone-based dermatological imaging. As AI-driven skin assessment tools grow in capability, a major limitation remains the variability in lighting, angle, and color fidelity in user-captured photos. To address this challenge, the team created a fold-flat, low-cost light box that attaches securely to a wide range of smartphones and provides uniform, high-CRI illumination suitable for clinical-grade analysis. Research into lighting technologies, human factors, smartphone specifications, and AI diagnostic tools guided the engineering decisions, ensuring the design aligned with real-world telemedicine and consumer use cases. The project emphasized meeting key performance requirements including illumination uniformity, color accuracy, portability, assembly time, cost of goods, and measurable image-quality improvement. Two complementary prototypes were developed to demonstrate both hardware feasibility and potential digital integration: a clip-on modular light box and a Raspberry Pi stand-alone interface that simulates app-based image processing. Usability testing with ten participants showed that the prototype met several design goals, such as compatibility, stability, and cost targets, while also revealing areas needing refinement, including alignment features and instruction clarity. The results confirm the feasibility of creating an affordable, standardized imaging environment that meaningfully enhances AI-assisted skin evaluation. The work completed in this project establishes a strong foundation for future iterations that will refine lighting uniformity, integrate electronics, and optimize ergonomics, ultimately supporting the development of a production-ready clinical imaging tool.