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.

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.

Models Supported: DenseNet121, DenseNet161, DenseNet169, DenseNet201 and DenseNet264 (1D and 2D version with DEMO for Classification and Regression)

Stack of REST APIs built on Flask for serving requests to MAMMORY (App), deployed on Azure with GitHub Actions (CI/CD)

Medical Images processing

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.

This project utilizes a sophisticated deep learning model trained to classify breast ultrasound images into three categories: benign, malignant, or normal, thus determining the presence of breast cancer.

Ensemble based transfer learning approach for accurately classifying common thoracic diseases from Chest X-Rays

Using Pytorch Lightning and Torchxrayvision's Pretrained Densenet121 Models

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.

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

This research enhances early disease diagnosis by analyzing retinal blood vessels in fundus images using deep learning. It employs eight pre-trained CNN models and Explainable AI techniques.

Innovation and Entrepreneurship Training Program for college students in 2019, ZengJin

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.