Project case study · 2026
Pneumonia X-ray Classifier
A reproducible computer-vision study comparing a custom CNN with established architectures across internal and external chest X-ray datasets.
Résumé-backed project-level results; architecture and dataset split not specified.
Problem
Pneumonia screening requires evaluation that accounts for class balance, external data behavior, model capacity, and operational recall.
Approach
The study trains a custom CNN, compares established architectures, selects thresholds on validation predictions, and tests internal and external datasets.
Architecture
dataset manifests → preprocessing → training and checkpoints → threshold selection → internal/external evaluation → complexity and result reports
Evaluation
Within this résumé-backed academic study—comparing a custom CNN with established architectures, selecting thresholds on validation predictions, and evaluating internal and external chest X-ray datasets—the reported project-level results are 0.941 ROC-AUC and 94.62% recall. The accepted evidence does not assign both headline metrics to a particular architecture or dataset split, so they should not be read as scores achieved across every internal and external evaluation.
Reproducibility
Reusable scripts cover training, evaluation, fine-tuning, dataset manifests, and model-complexity reporting so experiments can be repeated and compared through the same workflow.
Limitations
This is an academic prototype, not a medical device. Outcomes depend on dataset composition and external validation, and the results should not be interpreted as clinical performance.