Detecting and Extracting Brain Hemorrhages from CT Images Using Generative Convolutional Imaging Scheme.
V PandimuruganRajasoundaran SoundararajanSidheswar RoutrayA V PrabuHashem AlyamiAbdullah AlharbiSultan AhmadPublished in: Computational intelligence and neuroscience (2022)
The complex nature of CT images leads to noncorrelated feature complexities in diagnosis models. Considering the issue, the proposed system used GAN-based effective sampling techniques for enriching complex image samples into CNN training phases. This concludes the effective contribution of the proposed IGACM technique for detecting brain hemorrhages than the existing diagnosis models.
Keyphrases
- deep learning
- convolutional neural network
- image quality
- resting state
- computed tomography
- dual energy
- white matter
- contrast enhanced
- optical coherence tomography
- high resolution
- machine learning
- functional connectivity
- cerebral ischemia
- multiple sclerosis
- magnetic resonance
- mass spectrometry
- virtual reality
- photodynamic therapy
- pet ct