Brain Tumor and Alzheimer's Detection Flutter App

A Flutter app that classifies brain MRI scans as tumour, Alzheimer's or normal. A trained deep learning model analyses the uploaded image to support early diagnosis.

The user installs the app and signs in; credentials are checked against Firebase Authentication or the app's own API. Screens built in XML (or Jetpack Compose) collect the user's input and show results in lists and cards. Data is stored in Firebase Realtime Database / Firestore or a local SQLite database and synced when the phone is online. Notifications and background services alert the user to updates, and an admin side manages the records.

An Activity moves through onCreate, onStart, onResume (running), onPause, onStop and onDestroy. Saving state in onPause or onSaveInstanceState prevents data loss when the user switches apps or rotates the screen.

Firebase gives a hosted real-time database, authentication and push notifications without running our own server. SQLite stores data locally on the phone and works offline. We chose based on whether data must be shared between users.

Capture the frame, convert to grayscale, remove noise with a Gaussian blur, segment the region of interest with thresholding or edge detection, extract features and finally classify or match them.

First a detector (Haar cascade, HOG or a CNN) finds the face. Then a model converts it into an embedding, a list of numbers that describes the face, and compares it with stored embeddings using a distance measure.

We followed an iterative (incremental) model: requirements and a basic design first, then modules built and tested one by one, with the guide's feedback after each review. It suits a project where requirements become clearer as we build.

Unit testing of each module, integration testing of the complete flow and system testing with real inputs. Test cases with expected and actual results are listed in the report, including invalid inputs.

The current version is tested on a limited dataset or number of users. Future work includes deploying on the cloud, adding more features or languages, improving accuracy with more data and building a mobile version.