Project

Derm AI

Project Description

Derm AI, an intelligent skin diagnostics platform, was envisioned to bring AI-driven dermatology to the forefront of digital healthcare. The goal was to create a highly accurate, accessible, and user-friendly system that could classify skin conditions through image uploads. Our mission was to design and develop the platform from the ground up — integrating advanced deep learning models, a responsive UI, and explainable AI features — all tailored to enhance usability for both patients and medical professionals.

Project Specifics

  • Deep Learning & Image Classification
  • AI-Powered Web Application
  • Responsive UI/UX Design
  • Grad-CAM Visual Insights
Grad-CAM Explainability

Visual interpretation of AI predictions

Grad-CAM visualizations highlight the image regions that influenced the model’s decisions. This enhances transparency, helps dermatologists validate predictions, and builds user trust in the AI's diagnostic process.

Transfer Learning

Adapted from large-scale pretrained models

Derm AI utilized transfer learning with models trained on ImageNet, then fine-tuned for dermatological classification. This approach accelerated training, improved performance, and enabled accurate results even with limited medical image data.

Our mission was to develop an AI-powered dermatology platform from the ground up, focusing on accurate skin condition detection, an intuitive user interface, and seamless diagnostic flow that empowers both patients and clinicians.

Generalization & Robustness

Trained on diverse, real-world datasets

The model was exposed to a wide variety of skin tones, conditions, and lighting scenarios. Data augmentation techniques like rotation and brightness adjustments improved its adaptability and minimized overfitting.

Accuracy & Model Performance

High model performance

Achieved strong classification accuracy of 92.4%, validated on a balanced dataset using precision, recall, and F1-score metrics.
This performance indicates reliable AI support for dermatological image assessment.