Image Classification
Keras
English
medical-imaging
pneumonia-detection
chest-xray
tensorflow
transfer-learning
vgg16
Instructions to use vksfdc2024/pneumonia-detection-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use vksfdc2024/pneumonia-detection-model with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://vksfdc2024/pneumonia-detection-model") - Notebooks
- Google Colab
- Kaggle
Pneumonia Detection Model (VGG16)
A VGG16 model fine-tuned for binary classification of chest X-ray images.
Model Performance
| Metric | Value |
|---|---|
| AUC-ROC | 0.8625 |
| Accuracy | 0.7734 |
| F1-Score | 0.6116 |
| Recall | 0.7916 |
| Specificity | 0.7681 |
| Precision | 0.4983 |
Training Details
- Base Model: VGG16 (ImageNet pre-trained)
- Fine-tuning: 2-phase (feature extraction + fine-tuning)
- Dataset: RSNA Pneumonia Detection Challenge (26,684 patients)
- Input Size: 224x224x3
- Optimal Threshold: 0.4376 (Youden's J)
Usage
import tensorflow as tf
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(repo_id="vksfdc2024/pneumonia-detection-model", filename="best_pneumonia_model.keras")
model = tf.keras.models.load_model(model_path)
Live Demo
Try the model: Hugging Face Space
Author
Vamsee Krishna Kondapalli | AIML Program Capstone Project
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