## Pneumonia Detection from Chest X-Ray Images                                         

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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

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### Description

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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.

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<b>Model Parameters</b>

Machine Learning Library: Keras

Base Model              : InceptionV3 && Custom Deep Convolutional Neural Network

Optimizers              : Adam

Loss Function           : categorical_crossentropy

<b>For Custom Deep Convolutional Neural Network : </b>

<b>Training Parameters</b>

Batch Size              : 64

Number of Epochs        : 30

Training Time           : 2 Hours

<b>Output (Prediction/ Recognition / Classification Metrics)</b>

<b>Testing</b>

Accuracy (F-1) Score    : 89.53%

Loss                    : 0.41

Precision               : 88.37%

Recall (Pneumonia)      : 95.48% (For positive class)

<!--Specificity             : -->

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<a href="[https://i.imgur.com/km4MF3J.png](https://imgur.com/km4MF3J)"></a>

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#### Tools / Libraries

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Languages               : Python

Tools/IDE               : Google Colab

Libraries               : Keras, TensorFlow, Inception, ImageNet

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