Lung Cancer Detection

• Built a CNN Model that classifies patients into 3 classes according to Breast CT-scanned Images.
• Developed using python with its libraries Tensorflow, Keras, NumPy, and Pandas.
• Used the Deep learning model Convolutional Neural Network with some optimization methods.

demo.mp4

Getting the Dataset

1. Download the dataset: You can download the dataset from Kaggle or any other reliable source that provides lung cancer CT scan images.

2. Extract the dataset: After downloading, extract the dataset into the data directory within the project folder.

   mkdir -p data
   unzip /path/to/downloaded/dataset.zip -d data

3. Verify the dataset structure: Ensure that the dataset is structured correctly, with images organized into appropriate subdirectories for each class.

   Data/
   ├── The IQ-OTHNCCD lung cancer dataset/
   │   ├── Bengin cases/
   │   ├── Malignant cases/
   │   └── Normal cases/
   └── ...
   

Make sure you have the necessary permissions to download and use the dataset.

How to Run the Code

1. Clone the repository:

   git clone https://github.com/ThisIsMrIsmail/Lung-Cancer-Detection.git

2. Navigate to the project directory:

   cd Lung-Cancer-Detection

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the backend server:

   python api/app.py

5. Open the web app url on browser, and start testing:

   http://127.0.0.1:5000

Make sure you have Python and pip installed on your system.