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[null,"1.","Record Nr.",null,"UNINA9911007179803321"]
[null,null,"Autore",null,"Nandal Amita"]
[null,null,"Titolo",null,"Machine Learning in Medical Imaging and Computer Vision"]
[null,null,"Pubbl/distr/stampa",null,"Stevenage : , : Institution of Engineering & Technology, , 2023"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"1-83724-414-6"]
[{"$nulls":4},"1-5231-6297-X"]
[{"$nulls":4},"1-83953-594-6"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (349 pages)"]
[null,null,"Collana",null,"Healthcare Technologies Series"]
[null,null,"Disciplina",null,"006.31"]
[null,null,"Soggetti",null,"Machine learning"]
[{"$nulls":4},"Computer vision"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Title -- Copyright -- Contents -- About the editors -- Preface -- 1 Machine learning algorithms and applications in medical imaging processing -- 1.1 Introduction -- 1.2 Basic concepts -- 1.2.1 Machine learning -- 1.2.2 Stages for conducting machine learning -- 1.2.3 Types of machine learning -- 1.3 Proposed algorithm for supervised learning based on neuro-fuzzy system -- 1.3.1 Input factors -- 1.3.2 Output factors -- 1.4 Application in medical images (numerical interpretation) -- 1.5 Comparison of proposed approach with the existing approaches -- 1.6 Conclusion -- References -- 2 Review of deep learning methods for medical segmentation tasks in brain tumors -- 2.1 Introduction -- 2.2 Brain segmentation dataset -- 2.2.1 BraTS2012-2021 -- 2.2.2 MSD -- 2.2.3 TCIA -- 2.3 Brain tumor regional segmentation methods -- 2.3.1 Fully supervised brain tumor segmentation -- 2.3.2 Non-fully supervised brain tumor segmentation -- 2.3.3 Summary -- 2.4 Small sample size problems -- 2.4.1 Class imbalance -- 2.4.2 Data lack -- 2.4.3 Missing modalities -- 2.4.4 Summary -- 2.5 Model interpretability -- 2.6 Conclusion and outlook -- References -- 3 Optimization algorithms and regularization techniques using deep learning -- 3.1 Introduction -- 3.2 Deep learning approaches -- 3.2.1 Deep supervised learning -- 3.2.2 Deep semi-supervised learning -- 3.2.3 Deep unsupervised learning -- 3.2.4 Deep reinforcement learning -- 3.3 Deep neural network -- 3.3.1 Recursive neural network -- 3.3.2 Recurrent neural network -- 3.3.3 "]
[{"$nulls":4},"Convolutional neural network -- 3.4 Optimization algorithms -- 3.4.1 Gradient descent -- 3.4.2 Stochastic gradient descent -- 3.4.3 Mini-batch-stochastic gradient descent -- 3.4.4 Momentum -- 3.4.5 Nesterov momentum -- 3.4.6 Adapted gradient (AdaGrad) -- 3.4.7 Adapted delta (AdaDelta) -- 3.4.8 Root mean square propagation."]
[{"$nulls":4},"3.4.9 Adaptive moment estimation (Adam) -- 3.4.10 Nesterov-accelerated adaptive moment (Nadam) -- 3.4.11 AdaBelief -- 3.5 Regularizations techniques -- 3.5.1 l2 Regularization -- 3.5.2 l1 Regularization -- 3.5.3 Entropy regularization -- 3.5.4 Dropout technique -- 3.6 Review of literature -- 3.7 Deep learning-based neuro fuzzy system and its applicability in self-driven cars in hill stations -- 3.8 Conclusion -- References -- 4 Computer-aided diagnosis in maritime healthcare: review of spinal hernia -- 4.1 Introduction -- 4.2 Similar studies and common diseases of the seafarers -- 4.3 Background -- 4.4 Computer-aided diagnosis of spinal hernia -- 4.5 Conclusion -- References -- 5 Diabetic retinopathy detection using AI -- 5.1 Introduction -- 5.2 Methodology -- 5.2.1 Preprocessing -- 5.2.2 Feature extraction -- 5.2.3 Classification -- 5.2.4 Proposed method algorithm -- 5.2.5 Training and testing -- 5.2.6 Novel ISVM-RBF -- 5.3 Results and discussion -- 5.3.1 Dataset -- 5.3.2 Image processing results -- 5.3.3 Comparison with the state-of-the-art studies -- 5.4 Conclusion -- References -- 6 A survey image classification using convolutional neural network in deep learning -- 6.1 Introduction -- 6.2 Deep learning -- 6.2.1 Artificial neural network -- 6.2.2 Recurrent neural network -- 6.2.3 Feed forward neural network -- 6.3 Convolutional neural network -- 6.3.1 Convolutional layer -- 6.3.2 Pooling layer -- 6.3.3 Fully connected layer -- 6.3.4 Dropout layer -- 6.3.5 Softmax layer -- 6.4 CNN models -- 6.4.1 VGGnet -- 6.4.2 AlexNet -- 6.4.3 GoogleNet -- 6.4.4 DenseNet -- 6.4.5 MobileNet -- 6.4.6 ResNet -- 6.4.7 NasNet -- 6.4.8 ImageNet -- 6.5 Image classification -- 6.6 Literature survey -- 6.7 Discussion -- 6.8 Conclusion -- References -- 7 Text recognition using CRNN models based on temporal classification and interpolation methods -- 7.1 Introduction."]
[{"$nulls":4},"7.2 Related works -- 7.3 Datasets -- 7.4 Model and evaluation matrix -- 7.4.1 Process of data pre-processing -- 7.4.2 Air-writing recognition (writing in air) -- 7.5 Description and working of the model -- 7.5.1 Handwritten text recognition -- 7.6 Convolutional neural network -- 7.7 Connectionist temporal classification -- 7.8 Decoding -- 7.9 Optimal fixed length -- 7.10 Using different interpolation techniques for finding the ideal fixed frame length signals -- 7.11 CNN architecture -- 7.12 Evaluation matrix -- 7.12.1 Handwritten text recognition -- 7.12.2 Air-writing recognition -- 7.13 Results and discussion -- 7.13.1 Handwritten text recognition -- 7.13.2 Air-writing recognition -- 7.14 Conclusion -- References -- 8 Microscopic Plasmodium classification (MPC) using robust deep learning strategies for malaria detection -- 8.1 Introduction -- 8.1.1 Classification of Plasmodium using CNN -- 8.2 Related works -- 8.3 Methodology -- 8.3.1 Data preprocessing -- 8.3.2 Data augmentation -- 8.3.3 Weight regularization using batch normalization -- 8.3.4 Classification based on pattern recognition -- 8.3.5 Models for multi-class classification -- 8.4 Experimental results and discussion -- 8.4.1 Dataset description -- 8.4.2 Performance measures -- 8.5 Conclusion and future work -- References -- 9 Medical image classification and retrieval using deep learning -- 9.1 Medical images -- 9.1.1 Ultrasound images -- 9.1.2 Magnetic resonance imaging -- 9.1.3 X-ray imaging for pediatric -- 9.1.4 X-ray imaging for medical -- 9.2 Deep learning -- 9.2.1 Feed-forward neural networks -- 9.2.2 Recurrent neural networks -- 9.2.3 "]
[{"$nulls":4},"Convolutional neural networks -- 9.3 Deep learning applications in medical images -- 9.3.1 Identification of anatomical structures -- 9.3.2 Deep-learning-based organs and cell identification -- 9.3.3 Deep learning for cell detection."]
[{"$nulls":4},"9.4 Deep learning for segmentation -- 9.5 Conclusion -- References -- 10 Game theory, optimization algorithms and regularization techniques using deep learning in medical imaging -- 10.1 Introduction -- 10.2 Game theoretical aspects in MI -- 10.2.1 Cooperative games -- 10.2.2 Competitive games -- 10.2.3 Zero-sum and non-zero-sum games -- 10.2.4 Deep learning in game theory -- 10.3 Optimization techniques in MI -- 10.3.1 Linear programming -- 10.3.2 Nonlinear programming -- 10.3.3 Dynamical programming -- 10.3.4 Particle swarm optimization -- 10.3.5 Simulated annealing algorithm -- 10.3.6 Genetic algorithm -- 10.4 Regularization techniques in MI -- 10.5 Remarks and future directions -- 10.6 Conclusion -- References -- 11 Data preparation for artificial intelligence in federated learning: the influence of artifacts on the composition of the mammography database -- 11.1 Introduction -- 11.2 Federate learning -- 11.3 Methodology -- 11.4 Results -- 11.4.1 Discussion -- 11.5 Conclusions -- References -- 12 Spatial cognition by the visually impaired: image processing with SIFT/BRISK-like detector and two-keypoint descriptor on Android CameraX -- 12.1 Introduction -- 12.1.1 Contribution -- 12.2 Related work -- 12.3 Methodology -- 12.3.1 Problem formulation -- 12.3.2 Identification of all keypoints on the template image: SIFT-like approach -- 12.3.3 Identification of two keypoints to design the template image feature descriptor: BRISK-like approach -- 12.3.4 Fast binary feature matching -- 12.4 Implementation, results, and discussion -- 12.4.1 Implementation -- 12.4.2 Results and discussion -- 12.5 Conclusions -- Acknowledgments -- References -- 13 Feature extraction process through hypergraph learning with the concept of rough set classification -- 13.1 Introduction -- 13.2 Rough set theory -- 13.2.1 Preliminaries -- 13.3 Rough graph -- 13.4 Proposed work."]
[{"$nulls":4},"13.4.1 Rough hypergraph -- 13.4.2 Methodology -- 13.4.3 Experimental results -- 13.5 Results and discussion -- References -- 14 Machine learning for neurodegenerative disease diagnosis: a focus on amyotrophic lateral sclerosis (ALS) -- 14.1 Introduction -- 14.2 Neurodegenerative diseases -- 14.2.1 Alzheimer's disease -- 14.2.2 Parkinson's disease -- 14.2.3 Huntington's disease -- 14.2.4 Amyotrophic lateral sclerosis -- 14.3 The development stages of NDDs -- 14.4 Neuroimages on neurodegenerative diseases -- 14.4.1 Structural magnetic resonance -- 14.4.2 Diffusion tensor imaging -- 14.4.3 Functional magnetic resonance imaging -- 14.5 Machine learning and deep learning applications on ALS -- 14.6 Proposed research methodology -- 14.6.1 Methodology flow -- 14.6.2 Approaches to predictive machine learning -- 14.6.3 Discussion on review findings -- 14.7 Conclusion and future work -- References -- 15 Using deep/machine learning to identify patterns and detecting misinformation for pandemics in the post-COVID-19 era -- 15.1 Introduction -- 15.2 Literature review -- 15.2.1 Difference between misinformation and disinformation -- 15.2.2 Detection of fake news -- 15.3 Proposed approach -- 15.3.1 Neural networks -- 15.3.2 Convolutional neural network -- 15.3.3 Recurrent neural network -- 15.3.4 Random forest -- 15.3.5 Hybrid CNN-RNN-RF model -- 15.4 Methodology -- 15.4.1 Datasets -- 15.4.2 Data-cleaning -- 15.4.3 Feature extraction method -- 15.5 Proposed method -- 15.6 Comparison of models -- 15.6.1 Hyperparameter optimization method -- 15.6.2 Evaluation benchmarks -- 15.7 Future work -- 15.8 Conclusion -- References -- 16 Integrating medical imaging using "]
[{"$nulls":4},"analytic modules and applications -- 16.1 Introduction -- 16.2 Applications of medical imaging -- 16.2.1 Radiology and diagnostic imaging -- 16.2.2 Pathology -- 16.2.3 Cardiology -- 16.2.4 Neuroimaging."]
[{"$nulls":4},"16.2.5 Ophthalmology imaging."]
[null,null,null,"Sommario/riassunto","This edited book explores new and emerging technologies in the field of medical image processing using deep learning models, neural networks and machine learning architectures. Multimodal medical imaging and optimisation techniques are discussed in relation to the advances, challenges and benefits of computer-aided diagnoses."]