Breast Cancer Classifier App

A simple web-based image classification app built with Flask and Keras.
It allows users to upload an image, processes it with a trained CNN model, and returns the predicted class — all through an intuitive web interface.

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Table of Contents

• About
• Features
• Project Structure
• Setup
• Usage

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About

This project integrates a Convolutional Neural Network (CNN) model, and precisely the VGG19 model (I might change it in the future since this model is kinda overkill for this use case), with a Flask backend to classify images in real-time.
It’s designed as a minimal full-stack machine learning application — a foundation for deploying deep learning models as web services.

Key Components:

• 2 trained Keras models (vgg19.h5 and sequential_model_cnn.h5)
• A Flask API for serving predictions
• File upload and image preprocessing via OpenCV and NumPy
• Optional CORS setup for cross-origin frontend requests

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Features

• ✅ Upload any image through a browser or API request
• ✅ Preprocess image input using OpenCV (resize, normalize, reshape)
• ✅ Predict class probabilities with trained Keras models
• ✅ Return results as clean JSON or HTML
• ✅ Modular and easy to expand for other models or categories
• ✅ Runs locally with or without GPU (TensorFlow handles fallback)

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Project Structure

Click to expand

cnn_app/
├── app.py
├── models/
│ ├── sequential_model_cnn.h5
│ └── vgg19.h5
├── templates/
│ ├── predictions.html
│ └── index.html
├── requirements.txt
└── README.md

Setup

NOTE: This repo uses Git LFS to store large model files. Make sure you have Git LFS installed:

1. Clone the repository

git lfs install
git clone https://github.com/Sfeso13/Breast-Cancer-Classification-App.git

If you already cloned without LFS:

git lfs pull

2. Create and activate a virtual environment

cd Breast-Cancer-Classification-App
python3 -m venv venv

Linux/macos

source venv/bin/activate

or windows

venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

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Usage

1. Run the app

python app.py

Now you can visit

http://127.0.0.1:5000

And upload an image to get its classification