ECCNet

Deep learning experiments for esophageal cancer cell image classification using Keras/TensorFlow. See FinalReport.pdf for detailed methodology and results.

Repository Contents

• Data: Data_Generation.ipynb
• Experiments: Experiment_Model_AC_DA_GEN_46.ipynb, Experiment_ResNet.ipynb, Experiment_VGG.ipynb, Experiment_VIT.ipynb
• Prediction: ECCNetPrediction1.ipynb, ECCNetPrediction2.ipynb
• Models: model_ac_da_gen_46.keras, model.keras
• Report: FinalReport.pdf

Dataset

• Source: cbiocancer
• Storage:
  • Raw Dataset: Click Here
  • Generated Dataset: Click Here

To access the dataset in Colab:

from google.colab import drive
drive.mount('/content/drive')

Environment

Recommended: Google Colab with T4 GPU

Setup in Colab

1. Upload notebooks or connect Google Drive
2. Enable GPU: Runtime → Change runtime type → T4 GPU
3. Install dependencies if needed in colab.

Usage

Load trained models:

import tensorflow as tf
model = tf.keras.models.load_model('model.keras')

Run notebooks in Colab or Jupyter and ensure dataset paths are correctly configured.

References

See FinalReport.pdf for complete dataset description, methodology, and experimental results.

Contact

For questions or collaborations, please open an issue or contact the repository maintainer.