This project aims to solve the problem that many people tend to ignore early signs of skin conditions such as moles, rashes, spots, or lesions because of lack of awareness or reluctance to seek medical advice by developing an android application that can give information about skin disease by capturing a picture of skin conditions, then giving the name of the skin disease and treatment that can be done to the user, making skin disease diagnosis easier and more accessible.

• Project Overview
• Project Scope
• Technology Stack
• Dataset
• Main Repository
• Team Members
• Acknowledgments

• ML model can classify skin image into one of 7 diseases.
• Android app can take a picture using camera
• Android app can give information about the disease and treatment recomendation using data taken from database

• Tensorflow - Framework
• Google Colab - Development Environment
• Python - Programming Language

• Javascript - Programming Language
• Node js - Runtime Environment
• Express js - Framework
• Google Cloud Platform - Cloud Provider

• Kotlin - Programming Language
• Figma - Design Tool

We use datasets sourced from Harvard Dataverse (The HAM10000 dataset) and ISIC, which provide extensive collections of images for skin lesion analysis and classification.

• Machine Learning - https://github.com/Skinlyze/Skinlyze-ML.git
• Backend API - https://github.com/Skinlyze/Skinlyze-Backend.git
• Frontent Android - https://github.com/Skinlyze/Skinlyze-Frontend-Android.git

This project is a part of the Bangkit Academy 2024 capstone initiative by Team C242-PR613. We extend our heartfelt gratitude to our advisors, Mr. Firdhaus R. Azis and Mrs. Monifa Arini, for their guidance and support.

For more information, please contact:

• Muhammad Fauzan Naufal Ridho - c247b4ky2846@bangkit.academy