tf-image-classification-colorectal

README for Colorectal Histology Tissue Classification Project

Project Overview

This project aims to classify different types of tissues in rectal histology images using TensorFlow. By leveraging machine learning, specifically convolutional neural networks (CNNs), the project seeks to accurately identify and categorize various tissue types to aid in medical diagnoses and research.

Dataset

The dataset used in this project is the Colorectal Histology dataset, available through TensorFlow Datasets. The dataset contains labeled images of different types of colorectal tissues.

Project Structure

• Data Loading and Preparation:

  • The project starts by loading the Colorectal Histology dataset using TensorFlow Datasets.
  • Data augmentation and preprocessing steps are applied to enhance model performance.

• Model Development:

  • A Convolutional Neural Network (CNN) is developed using TensorFlow and Keras.
  • The model architecture is optimized through various hyperparameter tuning techniques.

• Training and Evaluation:

  • The model is trained on the preprocessed dataset, with performance metrics evaluated to ensure accuracy and reliability.
  • Evaluation metrics include precision, recall, F1-score, and confusion matrix analysis.

Usage

To run the project, follow these steps:

1. Clone the Repository:

   git clone <repository_url>

2. Install Dependencies: Ensure you have TensorFlow and TensorFlow Datasets installed. You can install them using pip:

   pip install tensorflow tensorflow-datasets

3. Run the Notebook: Open and run the provided Jupyter notebook colorectal_histology.ipynb to execute the entire workflow, from data loading to model evaluation.

Key Code Snippets

• Data Loading:

  import tensorflow as tf
  import tensorflow_datasets as tfds
  
  dataset, info = tfds.load("colorectal_histology", with_info=True, as_supervised=True, shuffle_files=True)

• Model Training:

  model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
  history = model.fit(train_dataset, epochs=10, validation_data=validation_dataset)

• Evaluation:

  test_loss, test_accuracy = model.evaluate(test_dataset)
  print(f"Test Accuracy: {test_accuracy}")

Resources

• TensorFlow Datasets: Colorectal Histology
• Colorectal Histology Dataset GitHub

Contributing

Contributions to improve the project are welcome. Feel free to submit pull requests or report issues.

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This README provides a concise overview of the project, including its purpose, usage instructions, and key code snippets to get started with classifying colorectal histology tissues using TensorFlow.