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
Luccas Menezzes developed and integrated a wound segmentation model for the datasci4citizens/server-wounds repository, focusing on automating image-based wound size estimation. He designed an end-to-end pipeline using Python and Keras, connecting virtual_views.py to wound_pixel_counter.py to preprocess images, run deep learning predictions, and compute wound area in pixels. This approach improved measurement accuracy and enabled automated wound assessment, laying the foundation for data-driven monitoring. Luccas demonstrated depth in backend development, image processing, and model deployment, delivering a modular solution that enhances the workflow for wound analysis. The work addressed a clear need for reliable, automated wound measurement from clinical images.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
398
Activity Months1
Your Network
11 peopleShared Repositories
11
Caio Maia Moreira SantosMember
caiomaia83Member
Andreas CisiMember
GoliasVictorMember
g169366Member
JoaoCelesteMember
Miguel BuzatoMember
GuilhermeMember
André SantanchèMember
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
Loading activity data...
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
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline