{"id":19480381,"url":"https://github.com/hasibzunair/3d-image-classification-tutorial","last_synced_at":"2025-10-28T21:08:29.362Z","repository":{"id":44360544,"uuid":"296429475","full_name":"hasibzunair/3D-image-classification-tutorial","owner":"hasibzunair","description":"[Official Keras Code Example] Tutorial to train a 3D CNN to predict presence of pneumonia from CT scans.","archived":false,"fork":false,"pushed_at":"2022-08-11T16:43:32.000Z","size":9540,"stargazers_count":58,"open_issues_count":0,"forks_count":24,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-04-03T23:41:13.514Z","etag":null,"topics":["convolutional-neural-networks","deep-learning","image-classification","keras"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/hasibzunair.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-09-17T19:59:24.000Z","updated_at":"2025-04-02T07:58:16.000Z","dependencies_parsed_at":"2022-09-05T22:20:21.616Z","dependency_job_id":null,"html_url":"https://github.com/hasibzunair/3D-image-classification-tutorial","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hasibzunair%2F3D-image-classification-tutorial","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hasibzunair%2F3D-image-classification-tutorial/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hasibzunair%2F3D-image-classification-tutorial/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hasibzunair%2F3D-image-classification-tutorial/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hasibzunair","download_url":"https://codeload.github.com/hasibzunair/3D-image-classification-tutorial/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250844343,"owners_count":21496554,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["convolutional-neural-networks","deep-learning","image-classification","keras"],"created_at":"2024-11-10T19:59:04.712Z","updated_at":"2025-10-28T21:08:29.294Z","avatar_url":"https://github.com/hasibzunair.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Tutorial on 3D Image Classification \n\n[![Hugging Face Spaces](https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/keras-io/3D_CNN_Pneumonia)\n\nLearn how to train a 3D convolutional neural network (3D CNN) to predict presence of pneumonia. Now on the Keras docs ([Link](https://keras.io/examples/vision/3D_image_classification/))!\n\n## Dataset\n\nThe dataset used in this tutorial is by [MosMedData: Chest CT Scans with COVID-19 Related Findings](https://www.medrxiv.org/content/10.1101/2020.05.20.20100362v1) which consists of 200 3D CT scans in total for the two classes. More detail [here](https://github.com/hasibzunair/3D-image-classification-tutorial/releases/tag/v0.2). Note that the data is public and I've kept it here for easy access/usage.\n\n## What is it about?\n\nThis tutorial will show the steps needed to build a 3D convolutional neural network (3D 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.\n\n## Usage\n\nYou can run the entire notebook on Colab! Copy the [URL](https://github.com/hasibzunair/3D-image-classification-tutorial/blob/master/3D_image_classification.ipynb) of the notebook [here](https://colab.research.google.com/github/). \n\n## Acknowlegements\n🤗 Spaces built by [Faizan Shaikh](https://github.com/faizankshaikh).\n\n## References\nUniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Severity Estimation ([Paper](https://arxiv.org/abs/2007.13224), [Code](https://github.com/hasibzunair/uniformizing-3D)).\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhasibzunair%2F3d-image-classification-tutorial","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhasibzunair%2F3d-image-classification-tutorial","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhasibzunair%2F3d-image-classification-tutorial/lists"}