Engineering Considerations for Real-World Deployment of AI-Based Skin Cancer Detection Software

Authors: Vishal Domale, Pooja Tupe

DOI: https://doi.org/10.37082/IJIRMPS.v14.i1.232892

Short DOI: https://doi.org/hbk6sc

Country: India

Full-text Research PDF File:   View   |   Download

Abstract: Early identification of skin cancer, particularly melanoma, is critical for improving patient survival rates, as the disease accounts for 75% of skin cancer-related deaths despite represent only 4% of total cases. While recent advancements in Deep Learning—specifically Convolutional Neural Networks (CNNs)—have achieved dermatologist-level diagnostic accuracy on benchmark datasets like HAM10000, significant engineering challenges remain for practical clinical deployment.
Moving beyond theoretical accuracy, we address critical deployment hurdles such as data variability, system scalability, and computational constraints in resource-limited healthcare settings. By employing transfer learning and optimized inference pipelines, we designed a cost-efficient, lightweight architecture tailored for practical use. This research provides a roadmap for transitioning AI-driven dermatological tools from the lab to effective, scalable real-world medical applications.

Keywords:

Paper Id: 232892

Published On: 2026-01-18

Published In: Volume 14, Issue 1, January-February 2026

Share this