{"id":15063758,"url":"https://github.com/kunalshelke90/xray_image_classification","last_synced_at":"2026-01-03T04:45:14.089Z","repository":{"id":256051492,"uuid":"854213470","full_name":"kunalshelke90/Xray_Image_Classification","owner":"kunalshelke90","description":"This project classifies chest X-ray images into Pneumonia and Normal using a CNN model. It includes deployment via Streamlit, enabling interactive web-based predictions and real-time analysis of X-ray images.","archived":false,"fork":false,"pushed_at":"2024-09-14T17:46:02.000Z","size":1669,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-01-22T08:37:32.717Z","etag":null,"topics":["classification","computer-vision","data-augmentation","deep-learning","docker","python","streamlit"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kunalshelke90.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-08T17:13:31.000Z","updated_at":"2024-09-14T17:46:05.000Z","dependencies_parsed_at":"2024-09-15T03:11:06.714Z","dependency_job_id":null,"html_url":"https://github.com/kunalshelke90/Xray_Image_Classification","commit_stats":null,"previous_names":["kunalshelke90/xray_image_classification"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kunalshelke90%2FXray_Image_Classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kunalshelke90%2FXray_Image_Classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kunalshelke90%2FXray_Image_Classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kunalshelke90%2FXray_Image_Classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kunalshelke90","download_url":"https://codeload.github.com/kunalshelke90/Xray_Image_Classification/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243779121,"owners_count":20346661,"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":["classification","computer-vision","data-augmentation","deep-learning","docker","python","streamlit"],"created_at":"2024-09-25T00:06:54.569Z","updated_at":"2026-01-03T04:45:14.024Z","avatar_url":"https://github.com/kunalshelke90.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Lungs Xray Classification with deep learning \n\n\nIn this project, I developed an X-ray lung classification model using deep learning techniques to detect lung abnormalities. Leveraging the PyTorch framework, I trained a convolutional neural network (CNN) to classify chest X-rays as either healthy or showing signs of lung disease. The model was then integrated into a web API for real-time predictions and deployed using Docker for scalability and accessibility.\n\n\n## worflow\n\n- constants : Defines global constants used across the project, such as paths, configurations, and hyperparameters.\n- config_entity : Contains configuration classes that manage various settings required for different stages of the workflow.\n- artifact_entity : Defines classes to represent artifacts generated at each stage of the machine learning pipeline.\n- components : Houses core modules for data ingestion, model training, evaluation, and prediction components.\n- pipeline :  Orchestrates the entire workflow by connecting various components and executing the machine learning pipeline.\n- main :  Entry point of the project that triggers the execution of the pipeline, handling end-to-end processing.\n\n## How to setup\n\n```bash\nconda create -p env python=3.8 -y\n```\n```bash\nconda activate env\n```\n```bash\ngit clone https://github.com/kunalshelke90/Xray_Image_Classification.git\n\n```\n```bash\ncd Xray_Image_Classification\n\n```\n```bash\npip install -r requirements.txt\n```\n- setup AWS CLI\n\n```bash\nhttps://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html\n```\n```bash\naws configure\n```\n\n- setup this information\n\n```bash\nAWS Access Key ID=\nAWS Secret Access Key=\nDefault region name=\n```\n- to run the code with streamlit\n\n```bash\npython app.py\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkunalshelke90%2Fxray_image_classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkunalshelke90%2Fxray_image_classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkunalshelke90%2Fxray_image_classification/lists"}