datasci4citizens/server-wounds
Jun 2025 – Jun 2025
1 Month active
Languages Used
Python
Technical Skills
Backend DevelopmentDeep LearningImage ProcessingMachine LearningModel DeploymentPython
lucas-vrm
PROFILE
Lucas-vrm
Developed and integrated a wound segmentation model for the datasci4citizens/server-wounds repository, focusing on automating image-based wound assessment. The work involved building an end-to-end pipeline using Python and Keras, where input images are preprocessed, passed through a deep learning model, and analyzed to compute wound area in pixels. This integration connected virtual_views.py and wound_pixel_counter.py, enabling accurate pixel counting and wound size estimation directly from images. The approach improved measurement accuracy and streamlined the workflow for wound monitoring, demonstrating proficiency in backend development, image processing, and model deployment while laying the foundation for future data-driven healthcare applications.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
398
Activity Months1
Your Network
12 peopleShared Repositories
12
Caio Maia Moreira SantosMember
caiomaia83Member
Andreas CisiMember
GoliasVictorMember
g169366Member
JoaoCelesteMember
Miguel BuzatoMember
GuilhermeMember
Roger HonoratoMember
Work History
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 — Server-wounds feature delivery focused on enhancing image-based wound assessment. Delivered a wound segmentation model and integrated it into the image processing pipeline to enable accurate wound size estimation from images. The integration uses wound_pixel_counter.py to load the Keras model, preprocess input images, run predictions, and compute wound area in pixels, with the workflow connected from virtual_views.py to acquire pixel counts for wound images. Overall, this work improves measurement accuracy, enables automation, and lays groundwork for data-driven wound monitoring.
2 Commits • 1 Features
Jun 1, 2025June 2025 — Server-wounds feature delivery focused on enhancing image-based wound assessment. Delivered a wound segmentation model and integrated it into the image processing pipeline to enable accurate wound size estimation from images. The integration uses wound_pixel_counter.py to load the Keras model, preprocess input images, run predictions, and compute wound area in pixels, with the workflow connected from virtual_views.py to acquire pixel counts for wound images. Overall, this work improves measurement accuracy, enables automation, and lays groundwork for data-driven wound monitoring.
June 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Backend DevelopmentDeep LearningImage ProcessingMachine LearningModel DeploymentPython
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