Pneumonia Detection from Chest X-Ray Images

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.

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)

https://i.imgur.com/km4MF3J.png">

Tools / Libraries

Languages               : Python
Tools/IDE               : Google Colab
Libraries               : Keras, TensorFlow, Inception, ImageNet