Skip to content
forked from adnaan619/Dev-V

A cross-platform skin disease detection mobile application built with Flutter and Firebase that uses deep learning to identify up to five common skin diseases. DermNet dataset was employed with a transfer learning approach using the ResNet-50 model.

Notifications You must be signed in to change notification settings

rusini666/CutiCare

 
 

Repository files navigation

CutiCare

A Flutter project that allows the user to diagnose five of the most prevalent skin diseases (Acne and Rosacea, Eczema, Melanoma Skin Cancer Nevi and Moles, Nail Fungus and other Nail Disease, and Psoriasis pictures Lichen Planus and related diseases).

Overview

The major goal of the project was to develop a cross-platform skin disease detection mobile application that could identify up to three prevalent skin diseases using deep learning. Our team was able to cover 5 skin illnesses using the DermNet dataset along with the transfer learning approach with ResNet-50 model, achieving a 60 percent accuracy. TensorFlow, a Python package, was used along with several other machine learning libraries, such as NumPy, Keras, and Pandas, which helped in the model's construction.

A few resources to get you started if this is your first Flutter project:

For help getting started with Flutter development, view the online documentation, which offers tutorials, samples, guidance on mobile development, and a full API reference.

About

A cross-platform skin disease detection mobile application built with Flutter and Firebase that uses deep learning to identify up to five common skin diseases. DermNet dataset was employed with a transfer learning approach using the ResNet-50 model.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 97.6%
  • Dart 2.4%