Derman : A Skin Cancer Detection App

We aim to provide a complete user experience with integrated medication and doctor-appointment APIs as well as awareness surrounding Skin cancer and allergies. Using 3 state-of-the-art Deep Learning Classification models we try to predict with considerably high accuracy if you are suffering from Melanoma (which is the most fatal type of skin cancer) or any other type of cancer (basal cell carcinoma), or if you are suffering from an allergy (using a separate model). With features like, embedded videos, remedies, medicine browsing, Find a Doctor we hope to be a one-stop solution for the user's dermatological problems.

Models:

We have used 3 models for 3-layered multipurpose testing, namely, for detecting Melanoma(the most fatal type of Skin Cancer), the given types of Skin Cancers:

1. Melanocytic nevi

2. Benign keratosis-like lesions

3. Basal cell carcinoma

4. Actinic keratoses

5. Vascular lesions

6. Dermatofibroma

And also the given types of allergies and skin diseases:

1. Acne and Rosacea Photos

2. Eczema and Atopic Dermatitis Photos

3. Nail Fungus and other Nail Disease

4. Scabies Lyme Disease and other Infestations and Bites

Application/Deployment:

To deploy our models we have seamlessly integrated Tensorflow models using TFLite framework into a Flutter Android App, where we have used multiple APIs to create an engaging user-experience and provided with testing/educative as well as preventive and medical aid features. The salient features of our Application is listed as follows:

1. Tutorial to explain process

2. User input form to check Symptoms

3. 3-types of Prediction model for reliable test results

4. Ailment information and awareness page

5. Contact Doctor through Email and Whatsapp APIs

6. Search for Local Hospitals using Google Map integration

7. Appointment real-time database and scheduling

Files in Repository:

1. Model Training, Data Preprocessing and Conversion to TFLite is shown in Skin-Cancer-Detection-App/Models/.. as Jupyter Notebooks. Data is taken from Kaggle for Melanoma Model and Cancer Model. For Allergy Model a custom scraper is implemented for obtaining data, which is shown in, Skin-Cancer-Detection-App/Models/allergy-model/.. A description of models is also provided.

2. Flutter app source code is contained in Skin-Cancer-Detection-App/app/..

3. Models in .tflite formats and their labels are in App/assets/models

Git LFS

To clone and use this repository, you'll need Git Large File Storage (LFS).

Our Developer Guide explains how to set up Git LFS for LSST development.

Install Instructions:

Requirements to run source code:

1. Flutter SDK and Android Studio or any other emulator to run app
2. Get packages by going to source code directory and run command: flutter pub get
3. To check is Flutter is running and emulator is connected: flutter doctor
4. To run app: flutter run

Additional Steps:

1. Obtain your SHA-keys and google-services.json after adding project name on Firebase Console
2. Add the files and ID in the downloaded code, this configures google login on homepage
3. Alternatively you could remove login code (dart files)
4. To make Google maps integration work you need to have a Google Cloud account

APK Submitted as zip file