ZENURA

Inspiration

Skin diseases affect millions worldwide, yet access to dermatologists remains limited, especially in remote areas. Many individuals rely on self-diagnosis, leading to delayed treatment. Inspired by the need for affordable, AI-driven healthcare, ZENURA was created to bridge this gap, offering instant, reliable skin disease detection using AI and deep learning.

What It Does

ZENURA allows users to upload images of skin conditions, which are analyzed using AI-powered models. A symptom-based questionnaire refines the results, providing a preliminary diagnosis, possible related conditions, and home remedies. The platform ensures quick, accessible, and reliable dermatological assessment for users.

How We Built It

ZENURA was developed using deep learning models for image classification, symptom-based decision algorithms, and cloud-based deployment. We utilized Python, TensorFlow, FastAPI, and cloud services to ensure scalability and accuracy. The frontend was built using React and Flutter, making it accessible via both web and mobile platforms.

Challenges We Ran Into

Data Collection & Model Training: Ensuring a diverse, high-quality dataset for accurate predictions.

Balancing Accuracy & Speed: Optimizing AI models for real-time diagnosis without performance trade-offs.

User Trust & Compliance: Implementing data privacy and security measures to meet healthcare standards.

Accomplishments That We're Proud Of

Successfully developing an AI-powered skin disease detection model with high accuracy.

Creating a user-friendly platform that makes dermatological assessment accessible and affordable.

Implementing a dual-layer diagnosis (image analysis + symptom questionnaire) for improved predictions.

What We Learned

The importance of diverse medical datasets to improve AI model accuracy.

Real-world healthcare challenges, including data privacy, compliance, and patient trust.

Optimizing AI models to work efficiently on low-resource devices for better accessibility.

What's Next for ZENURA

Expanding the dataset for improved accuracy across different skin tones and conditions.

Integrating dermatologist consultations for expert validation and treatment guidance.

Launching an enterprise API for hospitals, telemedicine platforms, and insurance companies.

Improving mobile accessibility to reach rural and underserved communities.

Built With

• ai
• deeplearning
• efficientnet
• kaggle
• machine-learning
• tensorflow