Images should be stricltly benign or malignant, as it is a binary classification

Any random image (non cancerious lesion) will produce random output. 

Skin lesion images taken without applying oilment and opening pores will produce incorrect output.

Please Note: This application is built for learning purpose

SkinGenics is a Skin-Cancer prediction application that uses CNN and Deep learning technology to calculate the probability that a skin lesions is Benign or Malignant, by comparing its appearance to thousands of other lesion images. SkinGenics has more than 96% accuracy

1. Upload Picture of Your Skin Lesion

2. SkinGenics will compare it with thousands of similar lesion images

3. SkinGenics will predict the class of cancer

1. 'Google-Cloud-VM-Instance' is used to deploy React App
   Frontend URL - https://cancer-detection-deep-learning.de.r.appspot.com/


2. 'Google-Cloud-Function' is used to serve backend (GCP Service)
   Backend URL - https://us-central1-cancer-detection-deep-learning.cloudfunctions.net/predict


3. Model is built using Jupyter Notebook on 'GCP VM Instance - AMD Machine'
   Instance IP - http://34.168.91.133:5000 (SSH Commands)


4. GCP Buckets are used to store dataset and save models (.h5)

1. Dataset : Contains ISIC 2018-HAM10000 Kaggle Dataset (nearly 33%)

2. Backend : Contains Fast-Api configuration to serve model

3. Frontend : Contains React.js codebase to serve UI

4. GCP : Contains Google-Cloud Configuration to serve backend

5. SavedModels : Stores model versions with various epochs and model architecture

6. Training : Stores notebook for current Convolutional Neural Network (CNN) Model