COVID-19 Detection Chest X-rays and CT scans: COVID-19 Detection based on Chest X-rays and CT Scans using four Transfer Learning algorithms: VGG16, ResNet50, InceptionV3, Xception. The models were trained for 500 epochs on around 1000 Chest X-rays and around 750 CT Scan images on Google Colab GPU. A Flask App was later developed wherein user can upload Chest X-rays or CT Scans and get the output of possibility of COVID infection.
COVID-19 Detection based on Chest X-rays and CT Scans using four Transfer Learning algorithms: VGG16, ResNet50, InceptionV3, Xception. Live Demo: http://coviddetector.kajadhav.me
Live Demo: http://coviddetector.kajadhav.me
COVID-19 Detection based on Chest X-rays and CT Scans using four Transfer Learning algorithms: VGG16, ResNet50, InceptionV3, Xception. The models were trained for 500 epochs on around 1000 Chest X-rays and around 750 CT Scan images on Google Colab GPU. After training, the accuracies acheived for the model are as follows:
InceptionV3 VGG16 ResNet50 Xception
Chest X-rays 96% 94% 83% 92%CT Scans 93% 93% 80% 95%
The article for the project was selected and published in Towards Data Science:
https://towardsdatascience.com/covid-19-detector-flask-app-based-on-chest-x-rays-and-ct-scans-using-deep-learning-a0db89e1ed2a
The dataset for the project was gathered from two sources:
For more screenshots, please visit the screenshots folder of my repo, or click here
ImageNet is formally a project aimed at (manually) labeling and categorizing images into almost 22,000 separate object categories for the purpose of computer vision research.
More information can be found here
ResNet50 ResNet-50 is a convolutional neural network that is 50 layers deep. You can load a pretrained version of the network trained on more than a million images from the ImageNet database. The pretrained network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. Unlike traditional sequential network architectures such as AlexNet, OverFeat, and VGG, ResNet is instead a form of “exotic architecture” that relies on micro-architecture modules.
More information can be found here
VGG16 is a convolutional neural network model proposed by K. Simonyan and A. Zisserman from the University of Oxford in the paper “Very Deep Convolutional Networks for Large-Scale Image Recognition”. The model achieves 92.7% top-5 test accuracy in ImageNet, which is a dataset of over 14 million images belonging to 1000 classes.
More information can be found here
Inception-V3 is a convolutional neural network that is 48 layers deep. You can load a pretrained version of the network trained on more than a million images from the ImageNet database. The pretrained network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. As a result, the network has learned rich feature representations for a wide range of images. The network has an image input size of 299-by-299.
More information can be found here
Xception is a convolutional neural network that is 71 layers deep. You can load a pretrained version of the network trained on more than a million images from the ImageNet database. The pretrained network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. As a result, the network has learned rich feature representations for a wide range of images. The network has an image input size of 299-by-299.
More information can be found here
