State of the Art in Neural Networks and Their Applications presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagnoses. 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 are covered, including relevant case studies. 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 1 covers the state-of-the-art deep learning approaches for the detection of renal, retinal, breast, skin, and dental abnormalities and more.
- Includes applications of neural networks, AI, machine learning, and deep learning techniques to a variety of imaging technologies
- Provides in-depth technical coverage of computer-aided diagnosis (CAD), with coverage of computer-aided classification, Unified Deep Learning Frameworks, mammography, fundus imaging, optical coherence tomography, cryo-electron tomography, 3D MRI, CT, and more
- Covers deep learning for several medical conditions including renal, retinal, breast, skin, and dental abnormalities, Medical Image Analysis, as well as detection, segmentation, and classification via AI
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Techn.
Dateigröße
ISBN-13
978-0-12-821849-5 (9780128218495)
Schweitzer Klassifikation
1. Computer Aided Detection of Abnormality in Mammography using Deep Object Detectors2. Detection of retinal abnormalities in fundus image using CNN Deep Learning Networks3. A survey of Deep Learning Based Methods for Cryo-electron Tomography Data Analysis4. Detection, Segmentation and Numbering of Teeth in Dental Panoramic Images with Mask RCNN5. Accurate Identification of Renal Transplant Rejection: Convolutional Neural Networks and Diffusion MRI6. Applications of the ESPNet Architecture in Medical Imaging7. Achievements of Neural Network in Skin Lesions Classification8. A Computer-aided-diagnosis System for Breast Cancer Molecular Subtypes Prediction in mammographic images9. Computer-Aided Diagnosis of Renal Masses10. Early Identification of Acute Rejection for Renal Allografts: A Machine Learning Approach11. Deep Learning for Computer-Aided Diagnosis in Ophthalmology: A Review12. Deep Learning for Ophthalmology using Optical Coherence Tomography13. Generative Adversarial Networks in Medical Imaging14. Deep Learning from Small Labeled Datasets Applied to Medical Image Analysis
