Radiomics and Deep Features: Robust Classification of Brain Hemorrhages and Reproducibility Analysis Using a 3D Autoencoder Neural Network
S Bijari, S Sayfollahi, S Mardokh-Rouhani, S Bijari… - Bioengineering, 2024 - mdpi.com
This study evaluates the reproducibility of machine learning models that integrate radiomics
and deep features (features extracted from a 3D autoencoder neural network) to classify …
and deep features (features extracted from a 3D autoencoder neural network) to classify …
Detection and Segmentation of Skull Fractures via CNN and U-Net Hybrid Model using Computed Tomography Images
T Kodavati, RP Kumar - 2023 Global Conference on Information …, 2023 - ieeexplore.ieee.org
Computer tomography (CT) are now widely used for the diagnosis of head various head
injuries. A CT scan slice has a lot of data that can't always be thoroughly analyzed quickly …
injuries. A CT scan slice has a lot of data that can't always be thoroughly analyzed quickly …
Automated Detection of Intracranial Hemorrhage using Convolutional Neural Networks
P Chakraborty, A Bandyopadhyay… - 2024 IEEE AITU …, 2024 - ieeexplore.ieee.org
Detecting intracranial hemorrhage promptly and accurately is vital for ensuring optimal
patient outcomes, especially in areas with limited access to radiologists. This paper …
patient outcomes, especially in areas with limited access to radiologists. This paper …