Deep Learning in Medical Image Processing and Analysis
portes grátis
Deep Learning in Medical Image Processing and Analysis
Dutta, Pushan Kumar; Rabie, Khaled; Karthik, Chandran; Chowdhury, Subrata
Institution of Engineering and Technology
10/2023
376
Dura
Inglês
9781839537936
15 a 20 dias
Descrição não disponível.
Chapter 1: Diagnosing and imaging in oral pathology by use of artificial intelligence and deep learning
Chapter 2: Oral implantology with artificial intelligence and applications of image analysis by deep learning
Chapter 3: Review of machine learning algorithms for breast and lung cancer detection
Chapter 4: Deep learning for streamlining medical image processing
Chapter 5: Comparative analysis of lumpy skin disease detection using deep learning models
Chapter 6: Can AI-powered imaging be a replacement for radiologists?
Chapter 7: Healthcare multimedia data analysis algorithms tools and techniques
Chapter 8: Empirical mode fusion of MRI-PET images using deep convolutional neural networks
Chapter 9: A convolutional neural network for scoring of sleep stages from raw single-channel EEG signals
Chapter 10: Fundamentals, limitations, and the prospects of deep learning for biomedical image analysis
Chapter 11: Impact of machine learning and deep learning in medical image analysis
Chapter 12: Systemic review of deep learning techniques for high-dimensional medical image fusion
Chapter 13: Qualitative perception of a deep learning model in connection with malaria disease classification
Chapter 14: Analysis of preperimetric glaucoma using a deep learning classifier and CNN layer-automated perimetry
Chapter 15: Deep learning applications in ophthalmology - computer-aided diagnosis
Chapter 16: Brain tumor analyses adopting a deep learning classifier based on glioma, meningioma, and pituitary parameters
Chapter 17: Deep learning method on X-ray image super-resolution based on residual mode encoder-decoder network
Chapter 18: Melanoma skin cancer analysis using convolutional neural networks-based deep learning classification
Chapter 19: Deep learning applications in ophthalmology and computer-aided diagnostics
Chapter 20: Deep learning for biomedical image analysis in place of fundamentals, limitations, and prospects of deep learning for biomedical image analysis
Chapter 2: Oral implantology with artificial intelligence and applications of image analysis by deep learning
Chapter 3: Review of machine learning algorithms for breast and lung cancer detection
Chapter 4: Deep learning for streamlining medical image processing
Chapter 5: Comparative analysis of lumpy skin disease detection using deep learning models
Chapter 6: Can AI-powered imaging be a replacement for radiologists?
Chapter 7: Healthcare multimedia data analysis algorithms tools and techniques
Chapter 8: Empirical mode fusion of MRI-PET images using deep convolutional neural networks
Chapter 9: A convolutional neural network for scoring of sleep stages from raw single-channel EEG signals
Chapter 10: Fundamentals, limitations, and the prospects of deep learning for biomedical image analysis
Chapter 11: Impact of machine learning and deep learning in medical image analysis
Chapter 12: Systemic review of deep learning techniques for high-dimensional medical image fusion
Chapter 13: Qualitative perception of a deep learning model in connection with malaria disease classification
Chapter 14: Analysis of preperimetric glaucoma using a deep learning classifier and CNN layer-automated perimetry
Chapter 15: Deep learning applications in ophthalmology - computer-aided diagnosis
Chapter 16: Brain tumor analyses adopting a deep learning classifier based on glioma, meningioma, and pituitary parameters
Chapter 17: Deep learning method on X-ray image super-resolution based on residual mode encoder-decoder network
Chapter 18: Melanoma skin cancer analysis using convolutional neural networks-based deep learning classification
Chapter 19: Deep learning applications in ophthalmology and computer-aided diagnostics
Chapter 20: Deep learning for biomedical image analysis in place of fundamentals, limitations, and prospects of deep learning for biomedical image analysis
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
diseases; image classification; medical image processing; convolutional neural nets; deep learning (artificial intelligence)
Chapter 1: Diagnosing and imaging in oral pathology by use of artificial intelligence and deep learning
Chapter 2: Oral implantology with artificial intelligence and applications of image analysis by deep learning
Chapter 3: Review of machine learning algorithms for breast and lung cancer detection
Chapter 4: Deep learning for streamlining medical image processing
Chapter 5: Comparative analysis of lumpy skin disease detection using deep learning models
Chapter 6: Can AI-powered imaging be a replacement for radiologists?
Chapter 7: Healthcare multimedia data analysis algorithms tools and techniques
Chapter 8: Empirical mode fusion of MRI-PET images using deep convolutional neural networks
Chapter 9: A convolutional neural network for scoring of sleep stages from raw single-channel EEG signals
Chapter 10: Fundamentals, limitations, and the prospects of deep learning for biomedical image analysis
Chapter 11: Impact of machine learning and deep learning in medical image analysis
Chapter 12: Systemic review of deep learning techniques for high-dimensional medical image fusion
Chapter 13: Qualitative perception of a deep learning model in connection with malaria disease classification
Chapter 14: Analysis of preperimetric glaucoma using a deep learning classifier and CNN layer-automated perimetry
Chapter 15: Deep learning applications in ophthalmology - computer-aided diagnosis
Chapter 16: Brain tumor analyses adopting a deep learning classifier based on glioma, meningioma, and pituitary parameters
Chapter 17: Deep learning method on X-ray image super-resolution based on residual mode encoder-decoder network
Chapter 18: Melanoma skin cancer analysis using convolutional neural networks-based deep learning classification
Chapter 19: Deep learning applications in ophthalmology and computer-aided diagnostics
Chapter 20: Deep learning for biomedical image analysis in place of fundamentals, limitations, and prospects of deep learning for biomedical image analysis
Chapter 2: Oral implantology with artificial intelligence and applications of image analysis by deep learning
Chapter 3: Review of machine learning algorithms for breast and lung cancer detection
Chapter 4: Deep learning for streamlining medical image processing
Chapter 5: Comparative analysis of lumpy skin disease detection using deep learning models
Chapter 6: Can AI-powered imaging be a replacement for radiologists?
Chapter 7: Healthcare multimedia data analysis algorithms tools and techniques
Chapter 8: Empirical mode fusion of MRI-PET images using deep convolutional neural networks
Chapter 9: A convolutional neural network for scoring of sleep stages from raw single-channel EEG signals
Chapter 10: Fundamentals, limitations, and the prospects of deep learning for biomedical image analysis
Chapter 11: Impact of machine learning and deep learning in medical image analysis
Chapter 12: Systemic review of deep learning techniques for high-dimensional medical image fusion
Chapter 13: Qualitative perception of a deep learning model in connection with malaria disease classification
Chapter 14: Analysis of preperimetric glaucoma using a deep learning classifier and CNN layer-automated perimetry
Chapter 15: Deep learning applications in ophthalmology - computer-aided diagnosis
Chapter 16: Brain tumor analyses adopting a deep learning classifier based on glioma, meningioma, and pituitary parameters
Chapter 17: Deep learning method on X-ray image super-resolution based on residual mode encoder-decoder network
Chapter 18: Melanoma skin cancer analysis using convolutional neural networks-based deep learning classification
Chapter 19: Deep learning applications in ophthalmology and computer-aided diagnostics
Chapter 20: Deep learning for biomedical image analysis in place of fundamentals, limitations, and prospects of deep learning for biomedical image analysis
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
