Medical Vision / Classification
PneumoScan AI.
Chest X-ray classification with MobileNetV2. An experiment with explicitly documented limitations.
Problem
Study pneumonia classification in chest X-rays.
Context
Medical deep learning experiment without diagnostic use or clinical validation.
Objective
Train MobileNetV2 and compare training and validation results.
Dataset / Data
Kaggle Chest X-Ray Pneumonia dataset. 5,216 training samples and only 16 validation samples in this experiment.
Architecture
X-ray → MobileNetV2 → classification. The exact framework remains to be confirmed.
Methodology
Training over 8 epochs. The peak validation observation at epoch 5 does not establish generalization on 16 images.
My contribution
Project reported in the candidate’s background. Individual scope and team responsibilities remain to be confirmed.
Challenges
Training / validation gap and uncertainty from a very small subset.
Limitations
Validation limited to 16 images. The observed 100% peak at epoch 5 is not evidence of real-world generalization. A larger independent test is needed.
What I learned
Interpret metrics in the context of evaluation set size and limitations.
Project results
Validation accuracy, epoch 8
Validation subset of only 16 images.
Training accuracy
5,216 training samples; 8 epochs.
Validation images
Insufficient to establish reliable generalization.