Cancer Detection Flask Application Development by oussema benayechCancer Detection Flask Application Development by oussema benayech

Cancer Detection Flask Application Development

oussema benayech

Completed work

AI Developer

OpenCV

scikit-learn

TensorFlow

Healthcare

Cancer Detection Flask Application

Technologies Used

Flask: Web server framework

TensorFlow: Deep learning framework

scikit-learn: Machine learning utilities

OpenCV: Image processing

Matplotlib: Visualization tools

HTML/CSS: Frontend interface

Installation

Python 3.8 or higher

pip package manager

Step 1: Clone the repository

git clone https://github.com/baga6312/cancer-recognizer-tensorflow.git
cd cancer-recognizer-tensorflow

Step 2: Create and activate a virtual environment (recommended)

python -m venv venv

# On Windows
venv\Scripts\activate

# On macOS/Linux
source venv/bin/activate

Step 3: Install dependencies

pip install -r requirements.txt

Step 4: Run the application

python app.py

API Usage

The application provides a RESTful API for programmatic access:

Endpoint: `/api/predict`

Training Your Own Model

To train the model on your own dataset:

Prepare your dataset in the following structure:

dataset/
├── cancer/
│   ├── image1.jpg
│   ├── image2.jpg
│   └── ...
└── non_cancer/
    ├── image1.jpg
    ├── image2.jpg
    └── ...

Run the training script:

python train_model.py --data_path dataset/ --epochs 20 --batch_size 32

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Completed work

Posted Jul 2, 2025

Developed a cancer detection app using Flask and machine learning tools.

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Cancer Detection Flask Application Development

Cancer Detection Flask Application

Technologies Used

Installation

Step 1: Clone the repository

Step 2: Create and activate a virtual environment (recommended)

Step 3: Install dependencies

Step 4: Run the application

API Usage

Endpoint: /api/predict

Training Your Own Model

Cancer Detection Flask Application Development

Cancer Detection Flask Application

Technologies Used

Installation

Step 1: Clone the repository

Step 2: Create and activate a virtual environment (recommended)

Step 3: Install dependencies

Step 4: Run the application

API Usage

Endpoint: /api/predict

Training Your Own Model

Endpoint: `/api/predict`

Endpoint: `/api/predict`