BarriBarri20/Lung-cancer-detection-model-training

This project is a deep learning model for lung cancer prediction, trained on a dataset containing images of different types of lung cancer and normal lung CT scans. The model was created using TensorFlow and Keras, and uses transfer learning with pre-trained models like ResNet50, VGG16, and MobileNetV2.

28
/ 100
Experimental

This project helps medical professionals, like radiologists or oncologists, classify CT-Scan images to identify different types of lung cancer or normal lung tissue. It takes CT-Scan images as input and outputs a classification indicating whether the image shows Adenocarcinoma, Large cell carcinoma, Squamous cell carcinoma, or normal cells. This tool can assist in the diagnostic process for lung cancer.

No commits in the last 6 months.

Use this if you need a pre-trained deep learning model to automatically categorize lung CT-Scan images for potential cancer detection.

Not ideal if you need a tool for medical image analysis beyond lung cancer classification or require a model trained on a different set of cancer subtypes.

radiology oncology medical imaging cancer detection CT-Scan analysis
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 8 / 25
Community 15 / 25

How are scores calculated?

Stars

12

Forks

4

Language

Jupyter Notebook

License

Last pushed

Apr 10, 2023

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/BarriBarri20/Lung-cancer-detection-model-training"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.