The project is based on Machine Learning.
The diagnosis in the application is made by using a CNN model for image prediction which is a Deep Learning algorithm, when the model is placed on a server and the application is a platform/ user interface that communicates with the server
A significant part of the population suffers from skin rashes. The queues for dermatologists are long (the average wait is about 20 days) and leave the person in uncertainty and discomfort.
SRD detects rashes and skin lesions caused by various reasons such as allergies, infectious diseases, and more.
The application gives a quick and accurate diagnosis and allays or verifies concern by showing the type of rash and its severity. It gives a rough estimate and is not a substitute for medical advice.
SRD works like this:
First, the user logs in to the application - only registered users can use it.
The application allows the user to take a picture or upload a picture from the gallery and when he chooses a picture the application sends it to the server,
the server receives it and activates our model the model makes a prediction for the picture and returns the classification to the application,
and the application shows the user the type of rash detected and if it could be life-threatening or there is no concern and in addition there is a link for more information.
We created a DataSet of five types of rashes by using images from 3 certified dermatology websites that approve the use of images:
- https://www.atlasdermatologico.com.br/browse.jsf
- https://dermnetnz.org/image-library
- https://www.kaggle.com/datasets/shubhamgoel27/dermnet
The 5 types of rashes we have chosen are rashes that are common in Israel, 3 of them indicate a potentially life-threatening situation and 2 of them do not.
We divided the pictures into 2: - The Train group - we will train the model with these pictures, and they are 80% percent of the total pictures,
When 90% of the images are used for training and 10% of the images are used for verification and improvement of the model. - The Test group which is used to test the model and evaluate it - what is its accuracy percentage and what is its error percentage.
And it is 20% of the total number of images.
In the project we want to demonstrate ability on 5 types of skin rushs.