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 API-driven 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.

Introduction

This project presents a cloud-based melanoma detection system using deep learning to provide fast, accurate skin lesion analysis accessible globally. A convolutional neural network (CNN) trained on large dermoscopic image datasets (HAM10000 and ISIC) classifies lesions as melanoma or non-melanoma. Integrated with cloud infrastructure, the system enables real-time image processing, remote access, and continuous improvement.

The platform features a user-friendly web interface for easy image upload and instant results, designed for both medical professionals and patients. Security measures ensure data privacy and compliance. The model achieved high accuracy (around 92%) and sensitivity, with deployment on a scalable cloud environment supporting broad accessibility.

Key strengths include leveraging established datasets and advanced AI techniques, while limitations involve dataset diversity, lack of clinical validation, and sensitivity to image quality. Overall, the system aims to improve early melanoma detection, making AI-assisted diagnostics more accessible, especially in underserved regions.

Conclusion

This research introduces a cloud-based melanoma detection system powered by deep learning, offering a fast, scalable, and accessible approach to skin cancer screening. The project combines a convolutional neural network (CNN) with real-time cloud deployment and a user-friendly interface to assist in early melanoma diagnosis. It leverages medical imaging datasets and cloud infrastructure to provide a reliable diagnostic aid accessible from anywhere. The key contributions and outcomes of the project are as follows: A. Accurate and Scalable Diagnostic System: • A custom-trained CNN model achieves high accuracy in classifying skin lesions, using publicly available datasets such as ISIC 2018. • The system is optimized for deployment on cloud platforms, allowing real-time analysis of uploaded images with rapid response times. B. Real-Time Cloud Integration: • Cloud deployment enables remote access and seamless integration of model inference and result delivery. • Users can upload dermoscopic images via a web interface and receive diagnostic feedback instantly, with results stored and managed securely. C. Privacy-Conscious and Efficient Design: • The system adheres to data privacy standards (e.g., HIPAA/GDPR), using anonymization and secure transmission protocols. • Efficient model architecture ensures low latency and reduced computational load, supporting concurrent users. However, the system’s performance is impacted by: • Dataset Limitations: Model generalization may be affected by limited diversity in skin tones and lesion types in the training data. • Environmental Dependency: The model requires high-quality images with proper lighting and focus for optimal predictions. • Clinical Integration Gap: The system has not yet been clinically validated or tested in real-world healthcare workflows. Aspect Generic Models Proposed System Diagnostic Accuracy Basic classification using limited models Custom CNN trained on benchmark datasets with high sensitivity Accessibility Offline or device-dependent tools Fully cloud-based with real-time web interface Data Handling Manual or local storage Secure, automated cloud-based data management User Interaction Limited usability Simple UI enabling quick uploads and instant results Compliance Often lacks data protection measures Built with HIPAA/GDPR principles in mind

References

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Copyright

Copyright © 2025 Varsha Nalla, Pallavi Kumari, Karthik Appala, Dr. J. Sudhakar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.