Welcome, to SkinScan

SkinScan is a user-friendly web application that empowers you to take control of your skin health. Simply snap a photo of any skin concern, and our advanced AI model will analyze the image and classify potential issues. SkinScan can classify any of the following skin diseases.

• Actinic Keratosis (AKIEC)
• Basal Cell Carcinoma (BCC)
• Benign Keratosis-like Lesions (BKL)
• Dermatofibroma (DF)
• Melanoma (MEL)
• Melanocytic Nevi (NV)
• Vascular Lesions (VASC)

How it works

Our Model Training Process:

• Preprocesses the CSV files with image paths and diagnoses
• Handles missing data in the HAM10000 dataset
• Creates train, validation and test sets
• DermaDataset class to load images and labels efficiently
• Pre-trained ResNet-50 model, a powerful Convolution neural network developed by Microsoft
• Fine tuned to match data
• Training_model function that handles the core training loop
• Adam optimizer, which helps the model learn by adjusting its parameters to minimize errors.
• Learning rate scheduler (ReduceLROnPlateau)
• Confusion matrix for model evaluation
• Classification report

How to run

Start by cloning the repo to your local machine

Git clone https://github.com/nathan-lioe/SkinScan.git

Download all Requirements

pip install -r requirements.txt

Create .env file

touch .env

Add your gemini api key as GEMINI = "YOUR_API_KEY"

Run Streamlit

streamlit run app.py

Make a prediction

To make a prediction with our fine-tuned model.You need to upload or take a picture on the Steamlit app. The results will be shown below.