Computational Analysis and Deep Learning for Medical Care. Группа авторов
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17.7 Conclusion 17.8 Applications References 18 Machine Learning and Deep Learning: Open Issues and Future Research Directions for the Next 10 Years 18.1 Introduction 18.2 Evolution of Machine Learning and Deep Learning 18.3 The Forefront of Machine Learning Technology 18.4 The Challenges Facing Machine Learning and Deep Learning 18.5 Possibilities With Machine Learning and Deep Learning 18.6 Potential Limitations of Machine Learning and Deep Learning 18.7 Conclusion Acknowledgement Contribution/Disclosure References Index
List of Illustrations
1 Chapter 1Figure 1.1 Architecture of LeNet-5.Figure 1.2 Architecture of AlexNet.Figure 1.3 Architecture of ZFNet.Figure 1.4 Architecture of VGG-16.Figure 1.5 Inception module.Figure 1.6 Architecture of GoogleNet.Figure 1.7 (a) A residual block.Figure 1.8 Architecture of ResNeXt.Figure 1.9 Architecture of SE-ResNet.Figure 1.10 Architecture of DenseNet.Figure 1.11 Architecture of MobileNets.
2 Chapter 2Figure 2.1 General architecture of a search engine.Figure 2.2 The increased mobile users.Figure 2.3 AI-powered location-based system.Figure 2.4 Architecture diagram for querying.
3 Chapter 3Figure 3.1 Phases of CECT images (1: normal liver; 2: tumor within liver; 3: sto...Figure 3.2 Architecture of convolutional neural network.Figure 3.3 AlexNet architecture.Figure 3.4 GoogLeNet architecture.Figure 3.5 Residual learning—building block.Figure 3.6 Architecture of ResNet-18.Figure 3.7 System model for case study on liver tumor diagnosis.Figure 3.8 Output of bidirectional region growing segmentation algorithm: (a) in...Figure 3.9 HA Phase Liver CT images: (a) normal liver; (b) HCC; (c) hemangioma; ...Figure 3.10 Training progress for AlexNet.Figure 3.11 Training progress for GoogLeNet.Figure 3.12 Training progress for ResNet-18.Figure 3.13 Training progress for ResNet-50.
4 Chapter 4Figure 4.1 Proposed system for image retrieval.Figure 4.2 Schematic of the deep convolutional neural networks.Figure 4.3 Proposed feature extraction system.Figure 4.4 Proposed model for the localization of the abnormalities.Figure 4.5 Graph for the retrieval performance of the metric learning for VGG19.Figure 4.6 PR values for state of art ConvNet model for CT images.Figure 4.7 PR values for state of art CNN model for CT images.Figure 4.8 Proposed system—PR values for the CT images.Figure 4.9 PR values for proposed content-based image retrieval.Figure 4.10 Graph for loss function of proposed deep regression networks for tra...Figure 4.11 Graph for loss function of proposed deep regression networks for val...
5 Chapter 6Figure 5.1 Different informatics in healthcare [28].
6 Chapter 6Figure 6.1 CT image reconstruction (past, present, and future) [3].Figure 6.2 (a) Classic machine learning algorithm, (b) Deep learning algorithm.Figure 6.3 Traditional neural network.Figure 6.4 Convolutional Neural Network.Figure 6.5 Psoriasis images [2].Figure 6.6 Restricted Boltzmann Machine.Figure 6.7 Autoencoder architecture with vector and image inputs [1].Figure 6.8 Image of chest x-ray [60].Figure 6.9 Regular thoracic disease identified in chest x-rays [23].Figure 6.10 MRI of human brain [4].
7 Chapter 7Figure 7.1 Architecture of the proposed approach.Figure 7.2 Sample Math dataset (including English characters).Figure 7.3 Sample Bangla dataset (including Bangla numeric).Figure 7.4 Sample Devanagari dataset (including Hindi numeric).Figure 7.5 Dataset distribution for English dataset.Figure 7.6 Dataset distribution for Hindi dataset.Figure 7.7 Dataset distribution for Bangla dataset.Figure 7.8 Dataset distribution for Math Symbol dataset.Figure 7.9 Dataset distribution.Figure 7.10 Precision-recall curve on English dataset.Figure 7.11 ROC curve on English dataset.Figure 7.12 Precision-recall curve on Hindi dataset.Figure 7.13 ROC curve on Hindi dataset.Figure 7.14 Precision-recall curve on Bangla dataset.Figure 7.15 ROC curve on Bangla dataset.Figure 7.16 Precision-recall curve on Math Symbol dataset.Figure 7.17 ROC curve on Math symbol dataset.Figure 7.18 Precision-recall curve of the proposed model.Figure 7.19 ROC curve of the proposed model.
8 Chapter 8Figure 8.1 Eye image dissection [34].Figure 8.2 Cataract algorithm [10].Figure 8.3 Pre-processing algorithm [48].Figure 8.4 Pre-processing analysis [39].Figure 8.5 Morphologically opened [39].Figure 8.6 Finding circles [40].Figure 8.7 Iris contour separation [40].Figure 8.8 Image inversion [41].Figure 8.9 Iris detection [41].Figure 8.10 Cataract detection [41].Figure 8.11 Healthy eye vs. retinoblastoma [33].Figure 8.12 Unilateral retinoblastoma [18].Figure 8.13 Bilateral retinoblastoma [19].Figure 8.14 Classification of stages of skin cancer [20].Figure 8.15 Eye cancer detection algorithm.Figure 8.16 Sample test cases.Figure 8.17 Actual working of the eye cancer detection algorithm.Figure 8.18 Melanoma example [27].Figure 8.19 Melanoma detection algorithm.Figure 8.20 Asymmetry analysis.Figure 8.21 Border analysis.Figure 8.22 Color analysis.Figure 8.23 Diameter analysis.Figure 8.24 Completed detailed algorithm.
9 Chapter 9Figure 9.1 Basic overview of a proposed computer-aided system.Figure 9.2 Block diagram of the proposed system for finding out liver fibrosis.Figure 9.3 Block diagram representing different pre-processing stages in liver f...Figure 9.4 Flow chart showing student’s t test.Figure 9.5 Diagram showing SegNet architecture for convolutional encoder and dec...Figure 9.6 Basic block diagram of VGG-16 architecture.Figure 9.7 Flow chart showing SegNet working process for classifying liver fibro...Figure 9.8 Overall process of the CNN of the system.Figure 9.9 The stages in identifying liver fibrosis by using Conventional Neural...Figure 9.10 Multi-layer neural network architecture for a CAD system