VivaKitPneumonia Detection from Chest X-rays
Transfer learning on 5,856 paediatric chest X-rays, patient-wise validation, Grad-CAM written from scratch, and a demo that shows why the model decided. Switch to a 3-class (bacterial / viral) problem with one config line.
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Our honest results
| ROC-AUC (test, 624 images) | 0.977 |
| Sensitivity - pneumonia caught | 0.997 |
| Specificity - normal cleared | 0.624 |
| Accuracy | 0.857 |
Yes, specificity is low: the official test set differs from the training data (dataset shift). The guide shows you why - and how to study it in your own extension. Trained in 4.6 min on an RTX 5070 Laptop GPU.
What's included
- Readable source codeSmall, commented modules you can explain line by line in your viva.
- Demo appA local web app to show your examiners - runs offline on your laptop.
- Trained model + reference resultsSo you can compare your own runs fairly.
- Learning guide with viva Q&ABackground, design choices explained, limitations and extension ideas.
- One-click setup & testsWindows setup script and tests that prove it works on your PC.
- Dataset download scriptFetches the public data from its original source, with the citation.
Dataset credit
Kermany D., Zhang K., Goldbaum M. (2018). Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification. Mendeley Data, V2. doi:10.17632/rscbjbr9sj.2 - CC BY 4.0.
Academic integrity
We sell code and teaching, not finished projects. You must understand, extend and write up the work yourself, acknowledge the kit and cite the dataset. No ready-made reports.