Automatic midplane finder and tissue segmentation for head CT scans
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Updated
Jun 8, 2020 - Jupyter Notebook
Automatic midplane finder and tissue segmentation for head CT scans
Tools to interpret CT scan of halite
Visual Volume visualizes volumetric data using Three.js and WebGL, rendering 3D data from sources like CT scans.
Train a 3D convolutional neural network to predict presence of pneumonia.
Adopted a convolutional neural network for COVID-19 testing. Examined the performance of different pre-trained models on CT testing and identified that larger, out-of-field datasets boost the testing power of the models.
A net providing information about stroke type using kt-scan image
Biomedical Image Processing involves applying computer algorithms to analyze and enhance medical images, such as X-rays or MRI scans. It aims to extract meaningful information, diagnose diseases, and aid in medical research by employing advanced image analysis techniques and computational tools.
Successful detection of Covid-19 using Chest X-Rays by building a Convolutional Neural Network (CNN) and visualising the world data using Covid-19 Trends.
Application for displaying and analyzing 3D volumes that utilizes custom made engine.
A combination of work done in our MPhys project and in the internship with the Christie NHS Foundation Trust over the summer.
Preprocessing the STOIC2021 dataset for detecting COVID-19 severity
Software for the OCT Scanner Project
Supervised Algorithms For The Detection Of COVID-19 From Chest CT & X-ray Scan Images
Deep-Learning solution for detecting Intra-Cranial Hemorrhage (ICH) 🧠 using X-Ray Scans in DICOM (.dcm) format.
The YOLOv4 is used for pancreas detection on CT-scans.
COVID-19 CT scan image classification using EfficientNetB2 with transfer learning and deployment using Streamlit. This project focuses on accurately classifying CT scan images into three categories: COVID-19, Healthy, and Others. Leveraging transfer learning on pretrained EfficientNetB2 models, the classification model achieves robust performance.
An implementation of a HIAS compatible xDNN classifier by Nitin Mane. Inspired by SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification by Eduardo Soares, Plamen Angelov, Sarah Biaso, Michele Higa Froes, Daniel Kanda Abe.
MediScan Mentor is an innovative application designed to assist medical students in interpreting CT scans.
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