Doctor-Patient Portal with Skin Cancer Prediction using Deep Learning

Abstract

Skin cancer is a serious issue that affects millions of people every year, putting a huge strain on healthcare systems around the worldit affects millions of people every year. The good news? Catching it early can save lives. But here’s the problem: traditional ways of diagnosing skin cancer can be slow and often require a specialist, which isn’t always easy to access. That’s where our Doctor-Patient Portal comes in.this platform uses Deep Learning—a fancy kind of AI—to help predict skin cancer. Patients can upload photos of their skin spots, and the system uses a Convolutional Neural Network (CNN) to figure out if the spots are harmless (benign) or potentially dangerous (malignant). But it’s not just about early detection. The portal also makes it easier for patients to talk to their doctors, schedule appointments, and manage their medical records. It’s all about making healthcare faster, smarter, and more accessible.

Introduction

Skin cancer is a widespread and serious disease where early detection greatly improves outcomes. However, traditional diagnosis can be slow, costly, and difficult due to limited dermatologist access. To address this, a Doctor-Patient Portal with Skin Cancer Prediction was developed, allowing patients to upload photos of skin spots that an AI analyzes using deep learning to determine if they are benign or malignant. The portal also offers healthcare management features like appointment booking, medical record storage, and doctor-patient communication.

The AI model is built using a Convolutional Neural Network (CNN) trained on the HAM10000 dataset, which contains over 10,000 labeled images of skin lesions. The CNN analyzes textures and patterns in the images to classify skin spots with high accuracy.

The portal architecture includes three main components: the AI-powered prediction module, the doctor-patient portal for managing health interactions, and a hospital locator tool using Google Maps API to facilitate in-person care.

Testing showed the AI model achieved 92.5% accuracy, 91.3% precision, and 93.2% recall, indicating reliable detection of potentially dangerous skin spots while minimizing false alarms. User feedback from patients and doctors was positive, praising ease of use and AI assistance in decision-making. Some limitations remain with rare lesion types and poor-quality images, but overall, the system improves early detection and access to care.

Conclusion

The Doctor-Patient Portal with Skin Cancer Prediction isn’t just another tech project—it’s a step toward making healthcare better, faster, and more accessible for everyone. By combining the power of AI with a patient-first approach, we’re giving people the tools they need to take control of their health and get the care they need, when they need it.Looking ahead, we’re really excited about what’s next. We’re planning to train the model on even more data, add support for other skin diseases, and even integrate real-time telemedicine features. Imagine being able to video chat with a dermatologist right from the portal! Our goal is simple: to make healthcare as easy and effective as possible for everyone.At the end of the day, this isn’t just about technology—it’s about people. It’s about giving patients the tools they need to stay healthy and helping doctors provide the best care possible. And that’s something we’re truly proud of.

References

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Copyright

Copyright © 2025 Nihal Ahamed M, Murali M , Poovarasan Y , Malathi M . 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.