Flutter Login/Signup with email and password

Description

Flutter Login/Signup with email and password

Flutter SDK is Google’s UI toolkit for crafting beautiful, natively compiled applications for mobile, web, and desktop from a single codebase, and Firebase is Google’s mobile application development platform that helps you build, improve, and grow your app. This Login/Signup with Email and Password authentication app is built using Flutter and Firebase. Firebase Authentication is used in this app for safe and secure user handling functionalities. This fully functional login/signup screen is suitable for any type of mobile application. you can start developing your own app by editing this project or you can get these functionalities from this project and use them in an existing developing project. some UI features such as frosted glass effect on UI components will bring an extra quality to your app, this app is developed to use the device screen size to render the widgets for a responsive design for any screen resolution. This app is developed using a Theme which you can change colors, fonts, font sizes from a single file and the whole app will change according to those changes and this will be very helpful if you are planning to give the user an option for Light Mode/Dark Mode UI experience.

Why This Flutter Application?

Login and Signup functionality is a major part in a mobile application. Using a pre-built Login/Signup function can easily save you time and with that you can pay more attention to other major components in your app. With this Flutter App we want to solve this basic need and problem at the same time so that you can focus on developing your core features.

Features:

Built using Flutter 3.3
GetX state management.
Easy Firebase integration.
Included assets (fonts, images).
Clear and easy codebase.
Validated text input fields.
Theme based App (colors, font size, etc, can be changed from a single file in code).
PDF with Step by step guide for easy setup.

Included User Interfaces

Splash Screen
Welcome Screen
Signup Screen
Login Screen
Home Screen

Official Libraries Used:

firebase_auth:
firebase_core:
get:

Other Libraries Used:

custom_clippers :
ndialog :
lottie :

Description

DR Scanner mobile app was developed with Flutter. In terms of code structure, it was written with the principle of clean code. The interface is simple and very easy to edit.

It can be run on both Android and iOS platforms.

It has an application structure that can be installed on Google Play Store, App Store and Huawei App Gallery platforms.

It can be easily run offline as there are no options such as database and remote connection.

Developed with a modern, simple user-friendly interface using the advantages of Flutter, DRScanner makes it easier for you to detect Diabetic Retinopathy. And in seconds!

All the steps that need to be done in detail are included in the documentation.

It should not be thought of as just an eye scanning application.

If the Tensorflow model is changed, it can also be used to detect other diseases.

High glucose level disrupts the structure of the retinal layer in the eyes and causes diabetic retinopathy which is characterized with new pathologic blood vessels in the eyes. Although diabetic retinopathy is not clear at the beginning of the disease, it is the most common problem in people who have diabetes, and causes blindness or cloudy vision if it is not diagnosed at the beginning of the disease. For early diagnosis of diabetic retinopathy, regular fundus controls and examination of the edema in the vessels of the retina are made periodically by ophthalmologists.

With in the scope of this study, it is made possible to provide the early diagnosis and the level of diabetic retinopathy by using deep learning, image processing methods and convolutional neural networks of the retina. In order to provide ease and rapid of diagnosis of the diabetic retinopathy in daily life, the diagnosis protocol has been turned into a mobile application. With the mobile application, both the diagnosis and more regular results of the diabetic retinopathy can be obtained easily and practically.

Dataset Information

Tag Number | Tag 0

Patient

Healthy

Steps Taken

All images are cropped and resized using the resize script and pre-processing script.
Images without retinopathy were projected using the rotation script; Images with retinopathy were reflected and rotated 90, 120, 180 and 270 degrees.
After rotating and reflecting with and without retinopathy, the class imbalance has been resolved and detected several thousand images have retinopathy.
In total, there are 5000 images processed by the neural network.
All images were converted to NumPy Arrays using the conversion script. NumPy Arrays combined images and tags in an array and send the images to CNN.
The model was created by using the TensorFlow and Keras libraries. For CNN, encoding was done by using anaconda as IDE and Jupyter Notepad within anaconda.
The pictures are tagged and parsed the pictures used to train them in two different sequences according to the labelling.
The pictures were then brought to a fixed size (255*255) by grayscale method .
The images are then passed through CNN and are called learning.
The trained model can be saved and then tested with pictures.

How to use?

Open drscanner file with Visual Studio Code
Run this command on the terminal flutter pub get
And finally flutter run

Information Schema

Demo Link:

Flutter Login/Signup with email and password

Description