https://github.com/aribornstein/keras-3d-image-classification

Based on the Keras 3D Image Classification from CT Scans by Hasib Zunair from the Keras IO examples repo
https://github.com/aribornstein/keras-3d-image-classification

3dimageclassification gridai keras tensorboard tensorflow

Last synced: 3 days ago
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Based on the Keras 3D Image Classification from CT Scans by Hasib Zunair from the Keras IO examples repo

• Host: GitHub
• URL: https://github.com/aribornstein/keras-3d-image-classification
• Owner: aribornstein
• License: apache-2.0
• Created: 2021-04-10T08:19:09.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2021-04-14T10:32:26.000Z (almost 4 years ago)
• Last Synced: 2025-01-21T17:08:22.296Z (4 days ago)
• Topics: 3dimageclassification, gridai, keras, tensorboard, tensorflow
• Language: Python
• Homepage:
• Size: 36.1 KB
• Stars: 3
• Watchers: 5
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Keras 3D Image Classification with TensorBoard Logging 

![3D Brain Scan Segments](https://keras.io/img/examples/vision/3D_image_classification/3D_image_classification_19_0.png)

## Run with Grid

[![Grid](https://img.shields.io/badge/rid_AI-run-78FF96.svg?labelColor=black&logo=data:image/svg%2bxml;base64,PHN2ZyB3aWR0aD0iNDgiIGhlaWdodD0iNDgiIGZpbGw9Im5vbmUiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+PHBhdGggZD0iTTEgMTR2MjBhMTQgMTQgMCAwMDE0IDE0aDlWMzYuOEgxMi42VjExaDIyLjV2N2gxMS4yVjE0QTE0IDE0IDAgMDAzMi40IDBIMTVBMTQgMTQgMCAwMDEgMTR6IiBmaWxsPSIjZmZmIi8+PHBhdGggZD0iTTM1LjIgNDhoMTEuMlYyNS41SDIzLjl2MTEuM2gxMS4zVjQ4eiIgZmlsbD0iI2ZmZiIvPjwvc3ZnPg==)](https://platform.grid.ai/#/runs?script=https://github.com/aribornstein/Keras3DImageClassifcation/blob/800e59c500af6a80c692d8a46b43c94505718adf/train.py&cloud=grid&instance=g4dn.xlarge&accelerators=1&disk_size=200&framework=tensorflow&script_args=grid%20train%20train.py%20--g_gpus%201%20--g_instance_type%20g4dn.xlarge%20--g_framework%20tensorflow%20--max_epochs%205)

This demo is a slighly modifed version of the [Keras 3D Image Classification from CT Scans

](https://keras.io/examples/vision/3D_image_classification/) by [Hasib Zunair](https://hasibzunair.github.io/) from the [Keras IO examples repo](https://github.com/keras-team/keras-io/tree/master/examples). I have added tensorboard logging and argparse to enable more training customization and hyperparameter tuning. 

The example shows the steps needed to build a 3D convolutional neural network (CNN) to predict the presence of viral pneumonia in computer tomography (CT) scans. 2D CNNs are commonly used to process RGB images (3 channels). A 3D CNN is simply the 3D equivalent: it takes as input a 3D volume or a sequence of 2D frames (e.g. slices in a CT scan), 3D CNNs are a powerful model for learning representations for volumetric data. It uses a subset of the [MosMedData: Chest CT Scans with COVID-19 Related Findings](https://arxiv.org/abs/2005.06465). This dataset consists of lung CT scans with COVID-19 related findings, as well as without such findings.

For more information please read the [original tutorial here](https://keras.io/examples/vision/3D_image_classification/)

## Running the Code

```

pip install -r requirements.txt

python train.py --max_epochs 3

```