rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Diagnostic biomedical signal and image processing applications with deep learning methods
  name:
    name part: Polat, Kemal
    role:
      role term: editor.
  name:
    name part: Öztürk, Şaban
    name part: 1989-
    role:
      role term: editor.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: ne
    date issued: 2023
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  abstract: Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods presents comprehensive research on both medical imaging and medical signals analysis. The book discusses classification, segmentation, detection, tracking and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT and X-RAY, amongst others. These image and signal modalities include real challenges that are the main themes that medical imaging and medical signal processing researchers focus on today. The book also emphasizes removing noise and specifying dataset key properties, with each chapter containing details of one of the medical imaging or medical signal modalities. Focusing on solving real medical problems using new deep learning and CNN approaches, this book will appeal to research scholars, graduate students, faculty members, R&D engineers, and biomedical engineers who want to learn how medical signals and images play an important role in the early diagnosis and treatment of diseases. Investigates novel concepts of deep learning for acquisition of non-invasive biomedical image and signal modalities for different disorders Explores the implementation of novel deep learning and CNN methodologies and their impact studies that have been tested on different medical case studies Presents end-to-end CNN architectures for automatic detection of situations where early diagnosis is important Includes novel methodologies, datasets, design and simulation examples.
  table of contents: Front Cover -- Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning Methods -- Copyright Page -- Contents -- List of contributors -- 1 Introduction to deep learning and diagnosis in medicine -- Introduction -- Deep learning architectures -- Convolutional neural network -- AlexNet -- ZFNet -- NiN -- VGGNet -- Inception (GoogLeNet) -- ResNet -- DenseNet -- U-Net -- SegNet -- R-CNN -- YOLO -- Other convolutional neural networks algorithms -- Recurrent neural network -- Long short-term memory -- Gated recurrent unit -- Bidirectional recurrent neural network -- Boltzmann machine and restricted Boltzmann machines -- Autoencoder -- Generative adversarial network -- Semisupervised GAN, bidirectional GAN -- Conditional GAN, InfoGAN, AC-GAN -- LAPGAN, DCGAN, BEGAN -- SAGAN, BigGAN -- WGAN, WGAN-GP, LSGAN -- PROGAN, StyleGAN, StyleGAN2 -- Comparisons of some GAN models -- Other architectures -- Deep belief network -- Capsule network -- Hybrid architectures -- Application fields of deep learning in medicine -- Clinical and medical images -- Biosignals -- Biomedicine -- Electronic health records -- Other fields -- Conclusions -- References -- 2 One-dimensional convolutional neural network-based identification of sleep disorders using electroencephalogram signals -- Introduction -- Materials and methods -- Dataset -- Method -- Results -- Discussions -- Conclusions -- References -- 3 Classification of histopathological colon cancer images using particle swarm optimization-based feature selection algorithm -- Introduction -- Methodology -- Dataset preparation -- Data preprocess and feature extraction -- Data size reduction -- Global feature extraction -- Classifier -- Gradient boosting -- Feature selection -- Particle swarm optimization -- Performance metrics -- Results -- Classification results -- Models complexity comparison.
  table of contents: SHAP analysis -- Receiver operator characteristic analysis -- Comparison -- Discussion -- Conclusion -- References -- 4 Arrhythmia diagnosis from ECG signal pulses with one-dimensional convolutional neural networks -- Introduction -- Definition of problem -- Materials and methods -- Dataset -- Oversampling -- 1D-CNN architecture -- 1D convolution layer -- Pooling layer -- Batch normalization and dropout layers -- Experimental result -- Performance metrics -- Experimental environment -- Random forest classifier -- 1D-CNN VGG16 classifier results -- Discussion -- Conclusion and future direction -- References -- 5 Patch-based approaches to whole slide histologic grading of breast cancer using convolutional neural networks -- Introduction and motivation -- Tubular formation -- Nuclear pleomorphism -- Mitotic figure detection and classification -- Challenges in obtaining Nottingham grading score -- Challenges in nuclear pleomorphism classification -- Challenges in detection/segmentation of tubular formation -- Challenges in mitotic classification -- Literature review and state of the art -- AI-based approaches for nuclear pleomorphism classification -- AI-based approaches for detection and segmentation of tubular formation -- AI-based approaches for mitotic classification and counting -- Problem/system/application definition -- Problem definition and description -- System and application definition -- Proposed methodology -- Pre-processing -- Deep learning methods -- Mitosis detection and classification -- Tubule segmentation -- Pleomorphism classification -- Results and discussions -- Dataset -- Assessment -- Quantitative assessment -- Qualitative assessment -- Conclusions -- Future work -- References -- 6 Deep neural architecture for breast cancer detection from medical CT image modalities -- Introduction -- Related work -- Experimental work -- Dataset.
  table of contents: Work flow -- Image pre-processing and augmentation methods -- Models explored -- Experimental results -- Evaluation parameters -- Models performance on BreakHis dataset -- Models performance on BACH2018 dataset -- Conclusion -- References -- 7 Automated analysis of phase-contrast optical microscopy time-lapse images: application to wound healing and cell motility... -- Introduction and motivation -- Literature review and state of the art -- Pre-processing of PCM time-lapse images -- Segmentation of PCM time-lapse images -- Tracking and quantification from PCM time-lapse images -- Workflows for the analysis of PCM time-lapse images -- Problem definition, acquisition and annotation of data -- Data acquisition -- Data annotation -- Proposed solution -- Pre-processing -- Segmentation -- Tracking and quantification -- Qualitative and quantitative analysis -- Pre-processing -- Segmentation -- Tracking and quantification -- Use cases and applications -- Discussion -- Conclusions -- Outlook and future work -- Software availability -- Acknowledgment -- References -- 8 Automatic detection of pathological changes in chest X-ray screening images using deep learning methods -- Introduction -- Screening for lung abnormalities -- Introduction -- Original image data -- Normal cases -- Pathological cases -- Image data preprocessing -- Methods -- Results -- Local conclusions -- Detecting extrapulmonary pathologies -- Introduction -- Data preparation -- Computational experiment -- Local conclusions -- Identification of subjects with lung roots abnormalities -- Introduction -- Materials -- Methods -- Results -- Local conclusions -- Chest X-ray image analysis web services -- Overview -- Authentication -- Authentication -- Input data validation -- X-ray modality checker for 2D images -- Anatomy checker for 2D images.
  table of contents: Axes order and orientation checker for 2D chest X-ray images -- Resources management -- Applications for processing and analyzing chest X-ray -- Lung segmentation on chest X-rays -- Detecting abnormalities in chest X-rays (heatmap) -- Application for computer-aided diagnostics based on chest X-ray -- Conclusion -- References -- 9 Dependence of the results of adversarial attacks on medical image modality, attack type, and defense methods -- Introduction -- Materials -- Chest X-ray images -- CT images -- Histopathology images -- Methods -- Attacks -- FGSM Attacks -- AutoAttacks -- Carlini-Wagner Attacks -- Defenses -- Adversarial training -- High-level representation guided denoiser -- The MagNet -- Experimental pipeline -- Results -- Experiments with X-ray images -- Experiments with computer tomography images -- Experiments with histopathology images -- Discussion -- The abilities of adversarial training defense method -- Important properties of class-label-guided denoiser defense -- Important properties of MagNet defense -- Conclusions -- References -- 10 A deep ensemble network for lung segmentation with stochastic weighted averaging -- Introduction -- Related works -- Proposed system -- Dataset collection -- Data augmentation -- Segmentation architectures -- HarDNet -- UNet++ -- Deeplab V3-ResNet -- Stochastic weighted averaging (SWA) -- Ensemble -- Results and discussion -- Dataset description -- Ablation studies -- Analysis of HarDNet -- Analysis of UNet++ -- Analysis of ResNet -- Analysis of ensemble -- Performance analysis -- Conclusion -- References -- 11 Deep ensembles and data augmentation for semantic segmentation -- Introduction -- Methods -- Deep learning for semantic image segmentation -- Loss functions -- Dice Loss -- Tversky Loss -- Focal Tversky Loss -- Focal Generalized Dice Loss -- Log-Cosh Type Losses -- SSIM Loss.
  table of contents: Different functions combined loss -- Data augmentation -- Shadows -- Contrast and motion blur -- Color mapping -- Experimental results -- Metrics -- Datasets and testing protocol for polyp segmentation -- Datasets and testing protocol for skin segmentation -- Datasets and testing protocol for leukocyte segmentation -- Experiments -- Conclusions -- Acknowledgment -- References -- 12 Classification of diseases from CT images using LSTM-based CNN -- Introduction -- Background -- CT dataset-issues and challenges in handling them -- Elucidating classical CNN- and LSTM-based CNN models -- Convolutional neural network -- Convolution layer -- Pooling layer -- Fully connected layers -- LSTM networks -- Previous work done on CNN-LSTM -- Conclusion -- References -- 13 A novel polyp segmentation approach using U-net with saliency-like feature fusion -- Introduction -- Methodology -- Image enhancement -- Discriminatory feature matrices -- Fusion of feature matrices -- U-net fine-tuning -- Loss function -- Experiments and results -- Datasets -- Evaluation metrics -- Experimental results of enhanced images with image inpainting method -- Experimental results of proposed method -- Discussion -- Conclusion -- Compliance with ethical standards -- Conflict of interest -- Human and animal rights -- References -- Index -- Back Cover.
  target audience: specialized
  note: edited by Kemal Polat, Şaban Öztürk.
  note: Includes bibliographical references and index.
  subject:
    topic: Artificial intelligence
    topic: Medical applications
  subject:
    topic: Deep learning (Machine learning)
  subject:
    topic: Diagnostic imaging
    topic: Data processing
  subject:
    topic: Electrophysiology
  subject:
    topic: Signal processing
  subject:
    topic: Biomedical engineering
  subject:
    topic: Deep Learning
  subject:
    topic: Electrophysiology
  subject:
    topic: Intelligence artificielle
    topic: Applications en médecine
  subject:
    topic: Apprentissage profond
  subject:
    topic: Imagerie pour le diagnostic
    topic: Informatique
  subject:
    topic: Électrophysiologie
  subject:
    topic: Traitement du signal
  subject:
    topic: Génie biomédical
  subject:
    topic: biomedical engineering
  subject:
    topic: Artificial intelligence
    topic: Medical applications
  subject:
    topic: Biomedical engineering
  subject:
    topic: Deep learning (Machine learning)
  subject:
    topic: Diagnostic imaging
    topic: Data processing
  subject:
    topic: Electrophysiology
  subject:
    topic: Signal processing
  classification: R859.7.A78 D53 2023
  classification: 610.285631
  related item:
    title info:
      title: Diagnostic biomedical signal and image processing applications with deep learning methods
    identifier: (OCoLC)1377211092
  related item:
    title info:
      title: Intelligent data centric systems
  identifier: 0323996817
  identifier: 9780323996815
  identifier: 9780323961295
  identifier: 9780323996815 Ingram Content Group
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: UKAHL
    record creation date: 230404
    record change date: 20260327174536.1
    record identifier: in00005771963
    language of cataloging:
      language term: eng