| Description |
1 online resource |
|
text rdacontent |
|
computer rdamedia |
|
online resource rdacarrier |
| Summary |
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 intelligence, deep learning, cognitive image processing, and suitable data analytics useful for clinical diagnosis and research applications. The application of neural network, artificial intelligence and machine learning methods in biomedical image analysis have resulted in the development of computer-aided diagnostic (CAD) systems that aim towards the automatic early detection of several severe diseases. State of the Art in Neural Networks and Their Applications is presented in two volumes. Volume One: Neural Networks in Oncology Imaging covers lung cancer, prostate cancer, and bladder cancer. Volume Two: Neural Networks in Brain Disorders and Other Diseases covers autism spectrum disorder, Alzheimer's disease, attention deficit hyperactivity disorder, hypertension, and other diseases. Written by experienced engineers in the field, these two volumes will help engineers, computer scientists, researchers, and clinicians understand the technology and applications of artificial neural networks. |
| 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. |
| Subject |
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
|
| Added Author |
Suri, Jasjit S., editor.
|
|
El-Baz, Ayman S., editor.
|
| Other Form: |
Print version: 9780128198728 |
| ISBN |
0128199121 |
|
9780128198728 (electronic bk.) |
|
0128198729 (electronic bk.) |
|
9780128199121 (electronic bk.) |
| Standard No. |
UKMGB 020802738 |
|
AU@ 000073396831 |
|
