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|a Diagnostic biomedical signal and image processing applications with deep learning methods /
|c edited by Kemal Polat, Şaban Öztürk.
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| 264 |
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1 |
|a London ;
|a San Diego, CA :
|b Academic Press, an imprint of Elsevier,
|c [2023]
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| 300 |
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|a 1 online resource
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|a Intelligent data-centric systems
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| 504 |
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|a Includes bibliographical references and index.
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| 588 |
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|a Description based on online resource; title from digital title page (viewed on August 23, 2023).
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|a 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.
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|a 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.
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|a 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.
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| 505 |
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|a 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.
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|a 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.
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| 520 |
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|a 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.
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| 650 |
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0 |
|a Artificial intelligence
|x Medical applications.
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| 650 |
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0 |
|a Deep learning (Machine learning)
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| 650 |
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0 |
|a Diagnostic imaging
|x Data processing.
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| 650 |
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|a Electrophysiology.
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| 650 |
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|a Signal processing.
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| 650 |
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|a Biomedical engineering.
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| 650 |
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2 |
|a Deep Learning
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| 650 |
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2 |
|a Electrophysiology
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| 650 |
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6 |
|a Intelligence artificielle
|x Applications en médecine.
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| 650 |
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6 |
|a Apprentissage profond.
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| 650 |
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6 |
|a Imagerie pour le diagnostic
|x Informatique.
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| 650 |
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6 |
|a Électrophysiologie.
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| 650 |
|
6 |
|a Traitement du signal.
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| 650 |
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6 |
|a Génie biomédical.
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| 650 |
|
7 |
|a biomedical engineering.
|2 aat
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| 650 |
|
7 |
|a Artificial intelligence
|x Medical applications
|2 fast
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| 650 |
|
7 |
|a Biomedical engineering
|2 fast
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| 650 |
|
7 |
|a Deep learning (Machine learning)
|2 fast
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| 650 |
|
7 |
|a Diagnostic imaging
|x Data processing
|2 fast
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| 650 |
|
7 |
|a Electrophysiology
|2 fast
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| 650 |
|
7 |
|a Signal processing
|2 fast
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| 655 |
|
7 |
|a Electronic books.
|2 local
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| 700 |
1 |
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|a Polat, Kemal,
|e editor.
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| 700 |
1 |
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|a Öztürk, Şaban,
|d 1989-
|e editor.
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| 710 |
2 |
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|a ScienceDirect (Online service)
|
| 776 |
0 |
8 |
|i Print version:
|t Diagnostic biomedical signal and image processing applications with deep learning methods
|z 9780323961295
|w (OCoLC)1377211092
|
| 830 |
|
0 |
|a Intelligent data centric systems.
|
| 856 |
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|z Connect to the full text of this electronic book
|t 0
|
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|a Elsevier ScienceDirect 2026-2027
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|a Texas A&M University
|b College Station
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|s www_evans
|d Available Online
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