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mediscan-efficientnetb4
monish211205/mediscan-efficientnetb4
mediscan-efficientnetb4 is a image classification model from monish211205. Use it when you need a label for an image. The card lists the license as mit.
Classifies chest X-rays as NORMAL or PNEUMONIA.
Access
Public
Updated Mar 28, 2026
Repo size
74.6 MB
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0
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Repo makeup
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.pth74.6 MB · 100%
At a glance
- Task
- Image Classification
- License
- mit
- Access
- Public
- Created
- Mar 28, 2026
- Updated
- Mar 28, 2026
- SHA
- 747b8565
Datasets
Model card
From the Hugging Face model README
MediScan AI — EfficientNetB4 Chest X-Ray Classifier
Classifies chest X-rays as NORMAL or PNEUMONIA.
Model Details
- Architecture: EfficientNetB4 (transfer learning, two-phase fine-tuning)
- Input: 380×380 RGB chest X-ray image
- Output: NORMAL | PNEUMONIA + confidence score
- Explainability: Grad-CAM heatmap overlay
Performance (Kaggle Chest X-Ray Test Set, n=624)
| Metric | Value |
|---|---|
| Accuracy | 87.66% |
| AUC-ROC | 0.9428 |
| Avg Precision | 0.9605 |
| Pneumonia Recall | 93.59% |
Training
- Dataset: Kaggle Chest X-Ray Images (Pneumonia) — 5,863 images
- Optimizer: AdamW + Cosine Annealing
- Epochs: 7 (early stopping)
- Hardware: Kaggle T4 GPU (8.5 min)
Usage
import torch
from inference import engine
engine.load("mediscan_v5.pth")
result = engine.predict(open("xray.jpg", "rb").read())
print(result["predicted_class"], result["confidence"])
Disclaimer
For research and educational purposes only. Not a certified medical device.
- Task
- Image Classification
- License
- mit
- Created
- Mar 28, 2026
- Updated
- Mar 28, 2026