COVID-19 Detection Using Chest X-Ray
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Updated
May 3, 2022 - Python
COVID-19 Detection Using Chest X-Ray
Classification and Gradient-based Localization of Chest Radiographs using PyTorch.
Use Deep Learning model to diagnose 14 pathologies on Chest X-Ray and use GradCAM Model Interpretation Method
Image classification on Satellite Dataset-RSI-CB256 with torchvision models.
Models Supported: DenseNet121, DenseNet161, DenseNet169, DenseNet201 and DenseNet264 (1D and 2D version with DEMO for Classification and Regression)
Independent Research Project on Automatic Detection Of Lumpy Skin Disease Using Deep Learning Techniques.
Leveraging the recent advances in machine learning and availability of public medical imaging datasets, we created a Free Online X-Ray Diagnostic Tool using deep learning that can determine the X-ray type and visualize the pathology.
Stack of REST APIs built on Flask for serving requests to MAMMORY (App), deployed on Azure with GitHub Actions (CI/CD)
Ensemble based transfer learning approach for accurately classifying common thoracic diseases from Chest X-Rays
Using Pytorch Lightning and Torchxrayvision's Pretrained Densenet121 Models
For Korean speech emotion detect, this model is trained by Korean dataset. There is no enough Korean dataset, so i tried to make this repo.
Medical Images processing
This repository is used to create Machine Learning models. Building three kinds of models that include covid detection, fruit and vegetable nutrition content, and general disease detection.
Building a powerful Neural network that can classify Natural Scenes around the world
Eye Disease Detection using Transfer Learning (DenseNet-121, EfficientNetB3, VGG-16, Resnet-152)
"Covid19-Detector" is a Django-ReactJS Web App with an Artificial Intelligence. It can detect COVID-19 from CT Scan Images using CNN based on DenseNet121 architecture.
TensorFlow-Android 经典模型从理论到实战(微课视频版)
A Deep Learning Projects For Diagnosis of COVID Disease by Chest X-Ray Images.
Facial Expression Recognition can be featured as one of the classification jobs people might like to include in the set of computer vision. The job of our project will be to look through a camera that will be used as eyes for the machine and classify the face of the person (if any) based on his current expression/mood.
Employing Error Level Analysis (ELA) and Edge Detection techniques, this project aims to identify potential image forgery by analyzing discrepancies in error levels and abrupt intensity changes within images.
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