anjanatiha / Pneumonia Detection From Chest X Ray Images With Deep Learning
Licence: mit
Detecting Pneumonia in Chest X-ray Images using Convolutional Neural Network and Pretrained Models
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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.
Code
GitHub Link : Detection of Pneumonia from Chest X-Ray Images(GitHub) GitLab Link : Detection of Pneumonia from Chest X-Ray Images(GitLab) Portfolio : Anjana Tiha's Portfolio
Dataset
Dataset Name : Chest X-Ray Images (Pneumonia) Dataset Link : Chest X-Ray Images (Pneumonia) Dataset (Kaggle) : Chest X-Ray Images (Pneumonia) Dataset (Original Dataset) Original Paper : Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning (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
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)
Sample Output:
See More Images
Confusion Matrix:
Tools / Libraries
Languages : Python Tools/IDE : Anaconda Libraries : Keras, TensorFlow, Inception, ImageNet
Dates
Duration : October 2018 - Current Current Version : v1.0.0.3 Last Update : 12.16.2018
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