Pneumonia detection from Chest-X-Ray images using Transfer Learning PyTorch, Flask, Docker, AWS.

Exploration of ML approaches for chest X-Ray image classification

Basic works on performing disease detection

The Chest Cancer Classification project diagnoses chest cancer from medical images using deep learning. It integrates MLflow for experiment tracking, DVC for version control, and Flask for backend processing. Docker and a CI/CD pipeline with GitHub Actions and AWS.

Covnet model for detection of Penumonia from Chest X-rays

An implementation of pix2pix GAN to improve Xray image quality

A CNN Algorithm will be used to detect pneumonia using chest Xrays.

Chest X-ray Images Classification with CNN

A model of the segmentation of the lung region from the x-ray images, the model relies on forming a mask through which that region can be cut.

The following study, through which we can generate X-ray images of the chest region in a semi-conditional manner, by taking advantage of the probability distributions.

ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification

Learning to Generalize towards Unseen Domains via a Content-Aware Style Invariant Framework for Disease Detection from Chest X-rays

An interactive visualization of machine learning model the modified vision transformer (under our research) on COVID-19 chest X-ray dataset (Sync display Revised with Dr. Hocking)

Screening for COVID-19 Infections using CT (Computed Tomography) and Chest X-Ray (CXR) images

People with pulmonary disease often have a high opacity, which makes segmentation of the lung from chest X-rays more difficult. In this study, I propose a methodology to improve the performance of the U-NET structure so that it is able to extract the features and spatial characteristics of the X-ray images of the chest region.

Covid-19 Recognition with Chest X-ray Images using ML and DL methods

Website for data science undergraduate capstone project "Improving Performance of Vision Encoding Large Language Models with Contextual Prompts" @UCSD HDSI 2024