AI-based system using CNNs to detect skin diseases from images. Trained on labeled data, it aids early diagnosis and supports dermatologists. Scalable for mobile/web healthcare platforms.
This project focuses on developing an AI-powered image classification system capable of detecting and predicting various types of skin diseases from medical images. Using Convolutional Neural Networks (CNNs), the system is trained on a dataset of preprocessed skin images, each labeled with a specific disease such as acne, eczema, or melanoma.
The model learns to identify unique patterns and features of different skin conditions, enabling accurate classification and early diagnosis. By automating this process, the system serves as a valuable diagnostic aid, offering quick assessments that can support dermatologists in clinical decision-making.
This solution is particularly beneficial in areas lacking access to specialized healthcare, providing a fast and cost-effective alternative. The trained model can be deployed in mobile or web applications, ensuring accessibility and scalability across platforms.
Ultimately, the Skin Disease Prediction System aims to reduce the manual workload of healthcare professionals, speed up diagnosis, and promote timely treatment, making it a significant step forward in digital healthcare innovation.
🔍 AI-based Image Classification using Convolutional Neural Networks (CNNs)
📸 Automatic Detection of skin diseases from medical images
🏷️ Multi-class Classification of conditions (e.g., acne, eczema, melanoma)
⚡ Fast & Accurate Diagnosis to assist dermatologists
📱 Deployable on Mobile/Web Platforms for remote access
🧠 Reduces Manual Workload and boosts clinical efficiency
🌍 Ideal for Low-Resource Settings with limited dermatology access
⏱️ Supports Early Diagnosis for better patient outcomes
💻 Scalable & Customizable for real-world healthcare applications
This AI-powered Skin Disease Prediction System bridges the gap between advanced diagnostics and accessible healthcare, empowering faster, smarter, and more inclusive medical support for everyone.
During the hackathon, we finalized our project idea: an AI-based Skin Disease Prediction System. We gathered and preprocessed image datasets covering multiple skin conditions, ensuring clean label distribution and handling data inconsistencies. A CNN model was built and trained for multi-class classification, incorporating image augmentation techniques to improve accuracy. We implemented model saving for future deployment and created a working training pipeline. The model’s performance was evaluated using test data, and we documented the entire process with visuals to support our final demonstration.
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