Pneumonia Detection from Chest X Ray Images with Deep Learning

Pneumonia Detection from Chest X-Ray Images using Transfer Learning

<pre> 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 </pre>

Description

<pre> 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. </pre>

Code

<pre> GitHub Link : <a href=https://github.com/anjanatiha/Detection-of-Pneumonia-from-Chest-X-Ray-Images>Detection of Pneumonia from Chest X-Ray Images(GitHub)</a> GitLab Link : <a href=https://gitlab.com/anjanatiha/Detection-of-Pneumonia-from-Chest-X-Ray-Images>Detection of Pneumonia from Chest X-Ray Images(GitLab)</a> Portfolio : <a href=https://anjanatiha.wixsite.com/website>Anjana Tiha's Portfolio</a> </pre>

Dataset

<pre> Dataset Name : Chest X-Ray Images (Pneumonia) Dataset Link : <a href=https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia>Chest X-Ray Images (Pneumonia) Dataset (Kaggle)</a> : <a href=https://data.mendeley.com/datasets/rscbjbr9sj/2>Chest X-Ray Images (Pneumonia) Dataset (Original Dataset)</a> Original Paper : <a href=https://www.cell.com/cell/fulltext/S0092-8674(18)30154-5>Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning</a> (Daniel S. Kermany, Michael Goldbaum, Wenjia Cai, M. Anthony Lewis, Huimin Xia, Kang Zhang) https://www.cell.com/cell/fulltext/S0092-8674(18)30154-5 </pre> <pre> <b>Dataset Details</b> 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)) <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 : --> </pre>

Sample Output:

<kbd> <img src=https://github.com/anjanatiha/Detection-of-Pneumonia-from-Chest-X-Ray-Images/blob/master/demo/sample/sample.png> </kbd> <kbd> <a href=https://github.com/anjanatiha/Detection-of-Pneumonia-from-Chest-X-Ray-Images/blob/master/demo/images/result.png>See More Images</a> </kbd>

Confusion Matrix:

<kbd> <img src=https://github.com/anjanatiha/Detection-of-Pneumonia-from-Chest-X-Ray-Images/blob/master/demo/report/CM.png alt="Confusion Matrix" width=800px height=600px> </kbd>

Tools / Libraries

<pre> Languages : Python Tools/IDE : Anaconda Libraries : Keras, TensorFlow, Inception, ImageNet </pre>

Dates

<pre> Duration : October 2018 - Current Current Version : v1.0.0.3 Last Update : 12.16.2018 </pre>