DermaScan

DermaScan website Homepage
photo upload
Skin disease prediction
Homemade remedies
Skin health scores
DermaScan System architecture
AI architecture and flow

🩺 Project Overview

Title:

DermaScan – Your AI-Powered Skin Health & Glow Companion

Short Description:

DermaScan is an AI-driven dermatology and skincare platform that empowers users to analyze, understand, and improve their skin health effortlessly. It uses computer vision, deep learning, and multimodal AI models to provide instant skin disease detection, beauty and glow analysis, and personalized skincare recommendations — all from a single selfie. Our goal is to make AI dermatology accessible, affordable, and trustworthy, bridging the gap between expert care and everyday skin awareness.

Economic / Financial Problem Addressed:

Access to dermatological care is costly, time-consuming, and geographically limited, especially in rural and semi-urban areas. Millions spend hundreds to thousands of rupees on unreliable skincare products or self-diagnosis without professional guidance, often worsening their condition.

DermaScan reduces this economic burden by:

Offering free or low-cost AI skin assessments through digital access.
Preventing early-stage conditions from escalating into costly medical treatments.
Supporting telehealth integration that saves consultation time and travel costs.
Enabling data-driven skincare that minimizes unnecessary product spending.

In essence, DermaScan provides affordable, preventive dermatology at scale — democratizing skin health for everyone.

Target Users & Use-Case Scenarios:

👩‍💻 Primary Users:

Individuals seeking personalized skincare advice.
Patients looking for early detection of skin conditions.
Dermatologists using AI reports for quick pre-diagnosis support.

🏥 Use-Case Scenarios:

Preventive Health: Users scan their face weekly to monitor acne, pigmentation, or eczema before escalation.
AI Beauty Companion: Influencers or professionals use glow scores and tone mapping for better skincare tracking.
Telehealth Extension: Doctors access DermaScan’s AI reports to speed up online consultations.
Smart Retail Integration: Cosmetic brands use insights to recommend suitable skincare products to customers.

Technology Stack Used:

Frontend:

React.js, Next.js, Tailwind CSS, Framer Motion → Built for fast, responsive, and visually pleasing user experience.

Backend:

Flask (Python) – connects frontend with AI services
MongoDB – stores user scans and analytics
Render – backend deployment
Vercel – frontend hosting

AI / ML Stack:

Convolutional Neural Networks (CNNs) for disease detection
TensorFlow, OpenCV, NumPy, Pandas, Scikit-learn for preprocessing and training
Google Gemini 2.5 Flash for multimodal reasoning, skincare insights, and NLP-based glow analysis
Model Optimization: TensorFlow Lite, data augmentation, and fairness calibration for diverse skin tones