{"id":25155191,"url":"https://github.com/biswa932/eczemadetectionusingcnn","last_synced_at":"2026-04-13T08:31:43.681Z","repository":{"id":214495773,"uuid":"736670845","full_name":"biswa932/EczemaDetectionUsingCNN","owner":"biswa932","description":"Machine Learning Model for Skin Condition Detection using CNN","archived":false,"fork":false,"pushed_at":"2024-01-08T20:27:07.000Z","size":64970,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-08-26T00:35:04.506Z","etag":null,"topics":["cnn","deep-learning","machine-learning","tensorflow"],"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/biswa932.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":"2023-12-28T14:40:04.000Z","updated_at":"2025-01-29T05:24:59.000Z","dependencies_parsed_at":"2024-01-08T20:56:02.963Z","dependency_job_id":null,"html_url":"https://github.com/biswa932/EczemaDetectionUsingCNN","commit_stats":null,"previous_names":["biswa932/eczemadetectionthreestage","biswa932/eczemadetectionusingcnn"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/biswa932/EczemaDetectionUsingCNN","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/biswa932%2FEczemaDetectionUsingCNN","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/biswa932%2FEczemaDetectionUsingCNN/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/biswa932%2FEczemaDetectionUsingCNN/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/biswa932%2FEczemaDetectionUsingCNN/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/biswa932","download_url":"https://codeload.github.com/biswa932/EczemaDetectionUsingCNN/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/biswa932%2FEczemaDetectionUsingCNN/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31746101,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-13T06:26:45.479Z","status":"ssl_error","status_checked_at":"2026-04-13T06:26:44.645Z","response_time":93,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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","deep-learning","machine-learning","tensorflow"],"created_at":"2025-02-09T00:40:43.589Z","updated_at":"2026-04-13T08:31:43.658Z","avatar_url":"https://github.com/biswa932.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Eczema Detection using 3 CNN model stages:\nThree stage detection of eczema\nThis program uses 3 stages/ models:\n1. Human Skin Detection model : if passed image is of human skin or not\n2. Eczema detection model: if passed image of human skin has eczema or not\n3. Eczema classification model: the level of eczema (mild, moderate, severe)\n\n# Pre-trained model used: \nhttps://www.kaggle.com/models/google/mobilenet-v2/frameworks/TensorFlow2/variations/140-224-feature-vector/versions/2\n\nThis pre-trained model \"mobilenet-v2\" has been retrained using new dataset and a single dense layer for each case.\n\n\n# Eczema images Dataset:\nThe eczema dataset was downloaded from: https://www.kaggle.com/datasets/shubhamgoel27/dermnet\n\n# Dataset structure: dataset/eczema_photos\n    1. allSkin: 2612 items\n    2. clearSkin: 1204 items\n    3. eczema: 1408 items\n    4. invalid: 2400 items\n    5. mild: 393\n    6. moderate: 641\n    7. severe: 373\n\n# Accuracy:\n1. Human Skin Detection model : Training(loss: 0.0086 - acc: 0.9995), Evaluation(loss: 0.0225 - acc: 0.9935)\n2. Eczema detection model: Training(loss: 0.0690 - acc: 0.9752), Evaluation(loss: 0.0823 - acc: 0.9674)\n3. Eczema classification model: Training(loss: 0.3789 - acc: 0.8927), Evaluation(loss: 0.8193 - acc: 0.6097)\n\n# All the models were converted to CoreML (*.mlmodel) using Colab.\n\n1. Code: ThreeStageEczemaDetection.ipynb\n2. CoreML convertion: ThreeStageEczemaDetectionToCoreML.ipynb\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbiswa932%2Feczemadetectionusingcnn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbiswa932%2Feczemadetectionusingcnn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbiswa932%2Feczemadetectionusingcnn/lists"}