natmourajr/CPE883-2025-02
Jul 2025 – Sep 2025
3 Months active
Languages Used
C++DockerfilePythonYAMLGitGit AttributesMarkdown
Technical Skills
Bug FixingCode NavigationCode RefactoringConfiguration ManagementCross-ValidationData Analysis
brennorcs
PROFILE
Brennorcs
Brennor Costa Santos developed a robust medical imaging experimentation platform in the natmourajr/CPE883-2025-02 repository, focusing on tuberculosis X-ray classification. He architected a reproducible Docker-based environment, implemented advanced data preprocessing and augmentation pipelines in Python, and integrated deep learning models such as CapsNet and Vision Transformer using PyTorch. His work included building modular evaluation workflows with ROC/AUC metrics, stratified K-Fold cross-validation, and demographic subgroup analysis, enabling data-driven model selection and reporting. Through careful code organization, refactoring, and documentation, Brennor improved maintainability and onboarding, while centralized configuration and experiment management accelerated iterative research and enhanced reliability for production deployment.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
36Total
Bugs
0
Commits
36
Features
12
Lines of code
6,110
Activity Months3
Your Network
22 people
Work History
September 2025
9 Commits • 3 Features
Sep 1, 2025Sep 2025 monthly summary for natmourajr/CPE883-2025-02. Focused on delivering robust evaluation capabilities, integrating advanced models, and improving project communications. The work enabled data-driven model selection, demographic-aware evaluation, and scalable reporting, driving measurable business value in model validation and deployment readiness.
9 Commits • 3 Features
Sep 1, 2025Sep 2025 monthly summary for natmourajr/CPE883-2025-02. Focused on delivering robust evaluation capabilities, integrating advanced models, and improving project communications. The work enabled data-driven model selection, demographic-aware evaluation, and scalable reporting, driving measurable business value in model validation and deployment readiness.
September 2025
August 2025
13 Commits • 5 Features
Aug 1, 2025August 2025 performance summary for natmourajr/CPE883-2025-02: Focused on delivering CapsNet-based X-ray modeling capabilities, refactoring data loading for TB X-ray datasets, and streamlining experiment workflows. Key deliverables include a CapsNet X-ray model suite with enhanced evaluation tooling (ROC plotting and StratifiedKFold integration), a revamped TB X-ray dataset and data loading pipeline, and a simplified, centralized experiment configuration and K-Fold workflow. Additionally, the repository structure was reorganized for maintainability, and the training loop was hardened with weight decay and per-epoch loss reporting. These efforts improve evaluation fidelity, reduce data-loading fragility, and accelerate iterative experimentation, driving higher reliability and business value in medical imaging research.
August 2025
13 Commits • 5 Features
Aug 1, 2025August 2025 performance summary for natmourajr/CPE883-2025-02: Focused on delivering CapsNet-based X-ray modeling capabilities, refactoring data loading for TB X-ray datasets, and streamlining experiment workflows. Key deliverables include a CapsNet X-ray model suite with enhanced evaluation tooling (ROC plotting and StratifiedKFold integration), a revamped TB X-ray dataset and data loading pipeline, and a simplified, centralized experiment configuration and K-Fold workflow. Additionally, the repository structure was reorganized for maintainability, and the training loop was hardened with weight decay and per-epoch loss reporting. These efforts improve evaluation fidelity, reduce data-loading fragility, and accelerate iterative experimentation, driving higher reliability and business value in medical imaging research.
July 2025
14 Commits • 4 Features
Jul 1, 2025Concise monthly summary for 2025-07 focused on delivering a reproducible development environment, data handling improvements, experimental framework integration, and advanced evaluation controls for TB image classification. The work lays a solid foundation for scalable experimentation, reliable model evaluation, and faster onboarding for the team.
14 Commits • 4 Features
Jul 1, 2025Concise monthly summary for 2025-07 focused on delivering a reproducible development environment, data handling improvements, experimental framework integration, and advanced evaluation controls for TB image classification. The work lays a solid foundation for scalable experimentation, reliable model evaluation, and faster onboarding for the team.
July 2025
Activity
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Quality Metrics
Correctness89.2%
Maintainability87.0%
Architecture85.6%
Performance79.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++DockerfileGitGit AttributesMarkdownPythonYAML
Technical Skills
Bug FixingCode NavigationCode OrganizationCode RefactoringComputer VisionConfiguration ManagementCross-ValidationData AnalysisData AugmentationData EngineeringData EvaluationData LoadingData PreprocessingData ScienceData Splitting
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