| Description |
1 online resource |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Note |
Includes index. |
| Contents |
Front Cover -- State of the Art in Neural Networks and Their Applications -- Copyright Page -- Dedication -- Contents -- List of Contributors -- Biographies -- Acknowledgments -- 1 Computer-aided detection of abnormality in mammography using deep object detectors -- 1.1 Introduction -- 1.2 Literature review -- 1.3 Methodology -- 1.3.1 Architectures of deep convolutional neural networks and deep object detectors -- 1.3.2 Abnormality detection with faster R-convolutional neural networks -- 1.3.3 Abnormality detection with YOLO -- 1.4 Experimental results -- 1.4.1 Data preparation |
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1.4.2 Abnormality detection with faster R-convolutional neural networks -- 1.4.3 Abnormality detection with YOLO -- 1.4.4 Results comparison -- 1.5 Discussions -- 1.6 Conclusion -- References -- 2 Detection of retinal abnormalities in fundus image using CNN deep learning networks -- 2.1 Introduction -- 2.2 Earlier screening and diagnosis of ocular diseases with CNN deep learning networks -- 2.2.1 Glaucoma -- 2.2.1.1 Methods and materials -- 2.2.1.2 Deep learning neural-network architectures for glaucoma screening and diagnosis |
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2.2.1.3 Application and evaluation on earlier glaucoma screening and diagnosis-classification -- 2.2.1.3.1 Fundus image glaucoma classification -- 2.2.1.3.2 Optical coherence tomography image glaucoma classification -- 2.2.1.4 Datasets used in glaucoma diagnosis -- 2.2.2 Age-related macular degeneration -- 2.2.2.1 Methods and materials -- 2.2.2.2 Deep learning-based methods for age-related macular degeneration detection and grading -- 2.2.3 Diabetic retinopathy -- 2.2.3.1 Methods and materials -- 2.2.3.2 Deep learning-based methods for diabetic retinopathy detection and grading |
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2.2.3.3 Dataset used diabetic retinopathy diagnosis -- 2.2.4 Cataract -- 2.2.4.1 Methods and materials -- 2.2.4.2 Deep learning-based methods for cataract detection and grading -- 2.3 Deep learning-based smartphone for detection of retinal abnormalities -- 2.3.1 Smartphone-captured fundus image evaluation -- 2.3.2 Deep learning-based method of ocular pathology detection from smartphone-captured fundus image -- 2.4 Discussion -- 2.5 Conclusion -- References -- 3 A survey of deep learning-based methods for cryo-electron tomography data analysis -- 3.1 Introduction -- 3.2 Deep learning-based methods |
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3.2.1 Detection and segmentation -- 3.2.2 Classification -- 3.2.3 Others -- 3.3 Conclusion -- References -- 4 Detection, segmentation, and numbering of teeth in dental panoramic images with mask regions with convolutional neural ne... -- 4.1 Introduction -- 4.2 Related work -- 4.3 Fédération Dentaire Internationale tooth numbering system -- 4.4 The method -- 4.4.1 Implementation details -- 4.4.1.1 Tooth numbering -- 4.5 Experimental analysis -- 4.5.1 Dataset -- 4.5.2 Evaluation -- 4.5.3 Results -- 4.6 Discussion and conclusions -- References |
| Subject |
Neural networks (Computer science)
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Diagnostic imaging -- Data processing.
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Image analysis.
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Neural Networks, Computer |
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Réseaux neuronaux (Informatique)
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Imagerie pour le diagnostic -- Informatique.
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Analyse d'images.
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Diagnostic imaging -- Data processing
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Image analysis
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Neural networks (Computer science)
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| Genre/Form |
Electronic book.
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| Added Author |
El-Baz, Ayman S.
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Suri, Jasjit S.
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| Other Form: |
Print version: 9780128218495 |
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Print version: 0128197404 9780128197400 (OCoLC)1204138450 |
| ISBN |
9780128218495 (electronic bk.) |
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0128218495 (electronic bk.) |
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9780128197400 |
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0128197404 |
| Standard No. |
AU@ 000069686188 |
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AU@ 000069791639 |
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