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
1. Detecção de COVID-19 a partir de imagens de raios X de tórax utlizando uma Deep Convolutional Neural Network otimizada.
GitHub Link : COVID-19 Detection from Chest X-Ray Images Linkedin : Antonio Esteves
Dataset Name : Chest X-Ray Images (Pneumonia) Dataset Link : Chest X-Ray Images (Pneumonia) Dataset (Kaggle) : Chest X-Ray Images (Pneumonia) Dataset (Original Dataset - No Labeled) Dataset Name : COVID-19 image data collection Dataset Link : COVID-19 image data collection (Original Dataset)
Detalhes do Dataset Nome do Dataset : Imagens de raio X de toráx (COVID-19) Número de Classes : 2 Número/Tamanho das imagens : Total : 178 (98.8 Megabyte (MB)) Treino : 76 (51.7 Megabyte (MB)) Validação : 30 (9.1 Megabyte (MB)) Teste : 72 (38.4 Megabyte (MB)) Parâmetros do Modelo Machine Learning Library : Keras Base Model : Custom Deep Convolutional Neural Network Otimizadores : Adam Função de Perda : categorical_crossentropy Deep Convolutional Neural Network Otimizada: Parâmetros de Treino Batch Size : 64 Número of Épocas : 100 Tempo de Treino : 40 Minutes Saída (Prediction/ Recognition / Classification Metrics) Teste F1-Score : 84.79% Accuracy : 83.33% Loss : 0.07 Precision : 82% Recall (COVID-19) : 86.11% (Para as classes positivas) Specificity : 80.56%
![](https://github.com/toniesteves/covid19-chest-x-ray-detection/raw/master/data/output/figures/sample.png)
![Confusion Matrix](https://github.com/toniesteves/covid19-chest-x-ray-detection/raw/master/data/output/figures/CM.png)
![ROC Curve](https://github.com/toniesteves/covid19-chest-x-ray-detection/raw/master/data/output/figures/ROC.png)
Languages : Python Libraries : Keras, TensorFlow, Inception, ImageNet
Duration : March 2020 - Current Current Version : v1.0.0.0 Last Update : 23.03.2020