Cloud-Based Melanoma Detection Using Deep Learning

Abstract: Early detection of melanoma significantly improves patient outcomes, and integrating modern AI with cloud computing offers a powerful solution to this challenge. This paper presents the design and development of a cloud-based melanoma detection system leveraging deep learning models for accurate and real-time diagnosis. The system utilizes convolutional neural networks (CNNs) trained on a large dataset of dermoscopic and clinical images to classify skin lesions. It is deployed on a scalable cloud infrastructure, enabling global access, remote image upload, and rapid inference. A modular APIdriven architecture ensures seamless communication between the frontend, model inference engine, and cloud storage. Emphasis is placed on data security, user authentication, and compliance with medical data privacy standards. Experimental evaluation demonstrates high accuracy and efficiency, supporting its application in telemedicine, dermatology clinics, and remote healthcare settings.