mediscan-efficientnetb4 – AI Model by monish211205 | AlphaNeural AI

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monish211205/
mediscan-efficientnetb4

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medical-imaging

pneumonia-detection

image-classification

chest-xray-pneumonia

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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

python

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.