COMPARISON OF MOBILENETV2 AND EFFICIENTNET-B0 ARCHITECTURES FOR BREAST CANCER CLASSIFICATION

This repository contains the code and trained models for breast cancer classification using mammography images from the CBIS-DDSM dataset. Two architectures are implemented: MobileNetV2 and EfficientNet-B0. The Jupyter Notebook was created with Google Colab, so for optimal running, please use Colab.

🚀 How to Run the Code

1. Clone the repository, if you want to

git clone https://github.com/your-username/Breast-Cancer-Deep-Learning.git
cd Breast-Cancer-Deep-Learning

2. Download the pth and CSV files for both models (You can find them in the Models folder)

3. Open the Inference Ready notebook with your Google Colab

4. Just upload those files (.pth, .CSV, and your own Kaggle.json) and run all of the cell.