Detecting Pneumonia in Chest X-ray Images using Convolutional Neural Network and Pretrained Models
Jupyter NotebookMIT
Pneumonia Detection from Chest X-Ray Images using Transfer Learning
Domain : Computer Vision, Machine Learning
Sub-Domain : Deep Learning, Image Recognition
Techniques : Deep Convolutional Neural Network, ImageNet, Inception
Application : Image Recognition, Image Classification, Medical Imaging
Description
1. Detected Pneumonia from Chest X-Ray images using Custom Deep Convololutional Neural Network and by retraining pretrained model “InceptionV3” with 5856 images of X-ray (1.15GB).
2. For retraining removed output layers, freezed first few layers and fine-tuned model for two new label classes (Pneumonia and Normal).
3. With Custom Deep Convololutional Neural Network attained testing accuracy 89.53% and loss 0.41.
Dataset Details
Dataset Name : Chest X-Ray Images (Pneumonia)
Number of Class : 2
Number/Size of Images : Total : 5856 (1.15 Gigabyte (GB))
Training : 5216 (1.07 Gigabyte (GB))
Validation : 320 (42.8 Megabyte (MB))
Testing : 320 (35.4 Megabyte (MB))
Model Parameters
Machine Learning Library: Keras
Base Model : InceptionV3 && Custom Deep Convolutional Neural Network
Optimizers : Adam
Loss Function : categorical_crossentropy
For Custom Deep Convolutional Neural Network : Training Parameters
Batch Size : 64
Number of Epochs : 30
Training Time : 2 Hours
Output (Prediction/ Recognition / Classification Metrics)Testing
Accuracy (F-1) Score : 89.53%
Loss : 0.41
Precision : 88.37%
Recall (Pneumonia) : 95.48% (For positive class)