Pneumonia-Classification-using-Deep-Learning

Detecting Pneumonia in Chest X-ray Images using CNNs and Pre-trained Models in Tensorflow

Motivation

Machine learning has a phenomenal range of applications, including in health and diagnostics. This is especially useful during these current times as COVID-19 is known to cause pneumonia.

Dataset

Dataset Name     : Chest X-Ray Images (Pneumonia)
Dataset Link     : Chest X-Ray Images (Pneumonia) Dataset (Kaggle)

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))

Results

Metric	Result
Accuracy (F-1) Score	89.53%
Loss	0.41
Precision	88.37%
Recall (Pneumonia)	95.48% (For positive class)

The model was built using InceptionV3 and Deep Convolutional Neural Network as its underlying architecture. I used Adam as the optimizer and categorical_crossentropy as loss function.

Sample output

The confusion matrix