pneumonia_detector

🩻 Pneumonia Detection Model (CNN)

🧠 Overview

This model is a Convolutional Neural Network (CNN) trained to classify chest X-ray images as either Normal or Pneumonia.
It demonstrates the application of deep learning to medical imaging classification and serves as an educational and research tool — not for clinical use.

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⚙️ Model Details

• Architecture: Custom CNN (3 convolutional + 2 dense layers)
• Framework: TensorFlow / Keras
• Input size: 150×150 RGB images
• Output: Binary classification — 0 = Normal, 1 = Pneumonia
• Dataset: Chest X-Ray Images (Pneumonia) — Kermany et al., Cell (2018)

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📚 Dataset Information

Source:
Kermany, D.S., Goldbaum, M., Cai, W. et al.
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,
Cell (2018), 172(5), 1122–1131.e9
DOI: 10.1016/j.cell.2018.02.010

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

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🩺 Intended Use

This model is designed for:

• Machine learning and data science education
• Research in medical image analysis
• Prototyping of AI healthcare applications

⚠️ Disclaimer:
This model is not a medical device and should not be used for clinical or diagnostic purposes.
It is provided solely for educational and research use.

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🧩 How It Works

1. The user uploads a chest X-ray image (.jpg or .png).
2. The image is resized to 150×150 pixels and normalized to [0, 1].
3. The CNN processes the image and outputs a probability:
   • 0 → Normal
   • 1 → Pneumonia
4. The final classification is the class with the highest confidence score.

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📊 Evaluation Report

🧾 Classification Metrics

Overall accuracy: 0.863
Macro F1-score: 0.841
Weighted F1-score: 0.856

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📉 ROC Curve

AUC = 0.959

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📊 Confusion Matrix

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💡 Example Usage

import tensorflow as tf
import numpy as np
from tensorflow.keras.preprocessing import image

# Load model
model = tf.keras.models.load_model("cnn_pneumonia.keras")

# Load and preprocess image
img_path = "test_image.jpg"
img = image.load_img(img_path, target_size=(150, 150))
img_array = image.img_to_array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)

# Predict
prediction = model.predict(img_array)[0][0]
label = "Pneumonia" if prediction > 0.5 else "Normal"
confidence = prediction if prediction > 0.5 else 1 - prediction

print(f"Prediction: {label} ({confidence:.2f} confidence)")