Richer and more representative cohorts
Expand beyond the Kaggle-hosted dataset with more institutions, broader age distributions, and richer clinical metadata so the model learns from a more realistic population.
Future Dev
Roadmap for making the engine stronger
This roadmap outlines the most meaningful ways to improve scientific rigor, generalization, usability, deployment resilience, and the overall human value of the solution.
Patient-level splitting and external validation
Move from folder-based held-out evaluation toward patient- or study-level splitting and test on external cohorts to strengthen any performance claims.
Benchmark stronger architectures
Compare MobileNetV2 against EfficientNet, ConvNeXt, and modern vision transformers while retaining calibration and explainability checks.
Improve evidence and uncertainty reporting
Add calibration plots, uncertainty estimates, richer saliency reporting, and clinician-friendly explanations so the system communicates confidence more responsibly.
Operational monitoring and safer deployment
Introduce request logging, model-version tracking, drift monitoring, and clearer audit trails so the application is easier to maintain as a production research service.
Embed the tool into real decision-support workflows
Design future iterations around clinical review, education, and triage support so the AI remains useful to people rather than becoming an isolated technical demo.
Priority Sequence
What should come first
- Strengthen the split and validation protocol before making stronger research claims.
- Expand the dataset and metadata foundation so the model sees more realistic variability.
- Improve calibration and explainability so users understand not just the answer, but how reliable it is.
- Harden backend deployment and observability once the scientific foundation is stronger.
Outcome Vision
What a stronger next version could become
Research-grade to real-world support
The most valuable future version of this project is one that combines stronger data, stricter evaluation, better transparency, and smoother deployment so it can genuinely support healthcare research, teaching, and intelligent triage exploration.
In short: better data, better validation, better explanations, better monitoring, and better human-centered design will make the engine more credible and more useful.