{"id":26407027,"url":"https://github.com/ecoronado92/medical_img_computer_vision","last_synced_at":"2026-02-13T03:45:44.238Z","repository":{"id":282321022,"uuid":"188294239","full_name":"ecoronado92/Medical_Img_Computer_Vision","owner":"ecoronado92","description":"machine-learning | CNN | ANN | deep-learning","archived":false,"fork":false,"pushed_at":"2020-02-24T13:45:11.000Z","size":36747,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-09-13T22:03:20.555Z","etag":null,"topics":["cnn","computer-vision","convolutional-neural-networks","machine-learning","transfer-learning","tumor-stains","xrays"],"latest_commit_sha":null,"homepage":"","language":"HTML","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/ecoronado92.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-05-23T19:28:18.000Z","updated_at":"2021-01-11T15:19:01.000Z","dependencies_parsed_at":"2025-03-14T00:36:58.673Z","dependency_job_id":null,"html_url":"https://github.com/ecoronado92/Medical_Img_Computer_Vision","commit_stats":null,"previous_names":["ecoronado92/medical_img_computer_vision"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ecoronado92/Medical_Img_Computer_Vision","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ecoronado92%2FMedical_Img_Computer_Vision","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ecoronado92%2FMedical_Img_Computer_Vision/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ecoronado92%2FMedical_Img_Computer_Vision/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ecoronado92%2FMedical_Img_Computer_Vision/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ecoronado92","download_url":"https://codeload.github.com/ecoronado92/Medical_Img_Computer_Vision/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ecoronado92%2FMedical_Img_Computer_Vision/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":275032801,"owners_count":25393761,"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","status":"online","status_checked_at":"2025-09-13T02:00:10.085Z","response_time":70,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["cnn","computer-vision","convolutional-neural-networks","machine-learning","transfer-learning","tumor-stains","xrays"],"created_at":"2025-03-17T17:28:50.423Z","updated_at":"2026-02-13T03:45:39.216Z","avatar_url":"https://github.com/ecoronado92.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"#  Medical Image Classification via Convolutional Neural Networks (CNN)\n\nRepo contains two examples for CNN for classifying medical images - X-rays and tumor stains.\n\nIn recent years, AI and Deep Learning have become promising methods to enhance Medical Image Classification tasks. The examples in this repo demonstrate the power of convolutional neural networks to classify abdominal and chest xrays by via transfer learning, leveraging ImageNet existing weights, and cancer tumor stains (malignant vs benign) without any transfer learning. The x-ray classification tasks is a simpler example given these are \u003c100 black and white images, while the tumor stain classification has increased complexity with a 1.6 Gb dataset of RGB images.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fecoronado92%2Fmedical_img_computer_vision","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fecoronado92%2Fmedical_img_computer_vision","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fecoronado92%2Fmedical_img_computer_vision/lists"}