State of the art in neural networks and their applications. Volume 2 /

State of the Art in Neural Networks and Their Applications, Volume Two presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagnoses. The book provides over views and case studies of advances in the role of machine learning, artificial in...

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Bibliographic Details
Corporate Author:	ScienceDirect (Online service)
Other Authors:	Suri, Jasjit S. (Editor), El-Baz, Ayman S. (Editor)
Format:	eBook
Language:	English
Published:	Amsterdam : Academic Press, 2023.
Subjects:	Diagnostic imaging > Data processing. Image analysis. Neural networks (Computer science) > Industrial applications. Imagerie pour le diagnostic > Informatique. Analyse d'images. Réseaux neuronaux (Informatique) > Applications industrielles. Diagnostic imaging > Data processing Image analysis Neural networks (Computer science) > Industrial applications Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

2.3 Deep learning applications in brain cancer
2.3.1 Tumor grading
2.3.2 Survival analysis
2.3.3 Radiogenomics
2.3.3.1 1p/19q
2.3.3.2 Isocitrate dehydrogenase
2.3.3.3 6-methylguanine-DNA methyltransferase
2.3.4 Pseudoprogression
2.4 Deep learning applications in breast cancer
2.4.1 Increasing accuracy in breast cancer risk assessment
2.4.2 Reproducible breast density assessment for improved breast cancer risk prediction
2.4.3 Improving performance in breast cancer diagnosis
2.4.4 Enhancing efficacy in breast cancer clinical practice
2.5 Conclusion
Acknowledgments
References
3 Deep neural networks and advanced computer vision algorithms in the early diagnosis of skin diseases
3.1 Introduction and motivation for the early diagnosis of melanoma
3.2 Artificial intelligence and computer vision in melanoma diagnosis
3.3 Medical diagnostic procedures for screening of skin diseases
3.4 State-of-the-art survey on skin mole segmentation methods
3.4.1 Comparison of the state of the art
3.4.2 Summary
3.5 Improved local and global patterns detection algorithms by deep learning algorithms
3.6 Early classification of skin melanomas in dermoscopy
3.6.1 Diagnostic algorithms
3.6.2 Approaches to detect the diagnostic criteria
3.6.3 Approaches to directly classify skin conditions
3.6.3.1 Classifiers utilizing the convolutional neural networks as a feature extractor
3.6.3.2 Classifiers using end-to-end learning convolutional neural networks model training with transfer learning
3.6.3.3 Convolutional neural networks model training from scratch
3.6.3.4 Ensembles of convolutional neural networks models
3.7 Conclusions
3.8 How to speed up the classification process with field-programmable gate arrays?
3.9 Challenges and future directions
3.10 Teledermatology.
6.5.4 Visual comparisons
6.5.5 Ablation study
6.6 Conclusion
References
7 Explainable deep learning approach to predict chemotherapy effect on breast tumor's MRI
7.1 Introduction
7.2 Materials and developed methods
7.2.1 Study population
7.2.2 Magnetic resonance imaging protocol
7.2.3 Image preprocessing
7.2.4 Convolution neural network architecture development
7.3 Results
7.3.1 Quantitative results
7.3.2 Qualitative results
7.4 Discussion
7.5 Conclusion
Aknowledgments
References
8 Deep learning interpretability: measuring the relevance of clinical concepts in convolutional neural networks features
8.1 Introduction
8.2 Related work on interpretable artificial intelligence
8.2.1 Motivations
8.2.2 Related terminology
8.2.3 Related work on explainable artificial intelligence
8.2.3.1 Explainable artificial intelligence for medical applications
8.2.3.2 Visualization methods and feature attribution
8.2.3.3 Concept attribution
8.2.4 Evaluation of explainable artificial intelligence methods
8.3 Methods
8.3.1 Retinopathy of prematurity
8.3.1.1 Relevant background
8.3.1.2 Dataset for the experiments
8.3.1.3 Task and classification model
8.3.2 Concept attribution with regression concept vectors
8.3.2.1 Identification of the concepts
8.3.2.2 Computing the regression concept vector
8.3.2.3 Generating local explanations by conceptual sensitivity
8.3.2.4 Agglomerating scores for global explanations
8.4 Experiments and results
8.4.1 Network performance on the retinopathy of prematurity task
8.4.2 Results of concept attribution
8.4.2.1 Identification of the concepts
8.4.2.2 Computation of the regression concept vectors
8.4.2.3 Evaluation of the conceptual sensitivities
8.4.2.4 Global explanations with Br
8.5 Discussion of the results.
8.6 Conclusions
Acknowledgments
References
9 Computational lung sound classification: a review
9.1 Introduction
9.2 Data processing
9.2.1 Audio signal preprocessing
9.2.1.1 Signal splitting
9.2.1.2 Noise filtering
9.2.1.3 Resampling
9.2.1.4 Amplitude scaling
9.2.1.5 Segment splitting
9.2.1.6 Padding
9.2.2 Feature extraction
9.2.2.1 Features for conventional classifiers
9.2.2.2 Time-frequency representations for deep learning
9.2.3 Data augmentation
9.2.3.1 Time domain
9.2.3.2 Time-frequency domain
9.3 Data modeling
9.3.1 Machine learning
9.3.1.1 Conventional classifiers
9.3.1.2 Deep learning architectures
9.3.1.2.1 Convolutional neural networks
9.3.1.2.2 Recurrent networks
9.3.1.2.3 Hybrid systems
9.3.2 Learning paradigm
9.3.2.1 Transfer learning
9.3.2.2 Postprocessing
9.4 Recent public lung sound datasets
9.4.1 ICBHI 2017 dataset
9.4.2 The Abdullah University Hospital 2020 dataset
9.4.3 HF_Lung_V1 dataset
9.5 Conclusion
References
10 Clinical applications of machine learning in heart failure
10.1 Introduction
10.2 Diagnosis
10.2.1 Automatic diagnosis, classification, and phenotyping of heart failure
10.2.2 Detection of heart failure-associated arrhythmia
10.3 Management
10.3.1 Prognostic prediction
10.3.2 Development of therapy
10.3.3 Optimal patient selection for specific therapies or recommendation of optimal therapy
10.4 Prevention
10.5 Conclusion
References
11 Role of artificial intelligence and radiomics in diagnosing renal tumors: a survey
11.1 Introduction
11.2 Basic background
11.2.1 Deep learning
11.2.2 Machine learning
11.2.3 Radiomics
11.3 Steps of artificial intelligence-based diagnostic systems
11.3.1 Image acquisition
11.3.2 Image segmentation.