Software for the OCT Scanner Project
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Updated
Aug 17, 2018 - C
Software for the OCT Scanner Project
Train a 3D Convolutional Neural Network to detect presence of brain stroke from 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.
A python class compatible with TensorFlow to perform data augmentation on 3D objects during CNN training.
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.
Deep-Learning solution for detecting Intra-Cranial Hemorrhage (ICH) 🧠 using X-Ray Scans in DICOM (.dcm) format.
LUng CAncer Screeningwith Multimodal Biomarkers
The YOLOv4 is used for pancreas detection on CT-scans.
Application for displaying and analyzing 3D volumes that utilizes custom made engine.
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.
Preprocessing the STOIC2021 dataset for detecting COVID-19 severity
Software for processing output of Scanco uCT machines/ any ISQ or TIF producing machine, and producing data. See instructions.pdf for full explaination.
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.
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.
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