State of the Art in Neural Networks and Their Applications

Volume 1

Ayman S. El-Baz, Jasjit Suri

• Publisher: Academic Press
• Year: 2021
• ISBN:
  9780128218495
• Topic: Biological Sciences
• Subtopic: Biotechnology
• Index: Biological Sciences

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

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Dedication
• List of Contributors
• Biographies
• Acknowledgments
• Chapter 1. Computer-aided detection of abnormality in mammography using deep object detectors
• Chapter 2. Detection of retinal abnormalities in fundus image using CNN deep learning networks
• Chapter 3. A survey of deep learning-based methods for cryo-electron tomography data analysis
• Chapter 4. Detection, segmentation, and numbering of teeth in dental panoramic images with mask regions with convolutional neural network features
• Chapter 5. Accurate identification of renal transplant rejection: convolutional neural networks and diffusion MRI
• Chapter 6. Applications of the ESPNet architecture in medical imaging
• Chapter 7. Achievements of neural network in skin lesions classification
• Chapter 8. A computer-aided diagnosis system for breast cancer molecular subtype prediction in mammographic images
• Chapter 9. Computer-aided diagnosis of renal masses
• Chapter 10. Early identification of acute rejection for renal allografts: a machine learning approach
• Chapter 11. Deep learning for computer-aided diagnosis in ophthalmology: a review
• Chapter 12. Deep learning for ophthalmology using optical coherence tomography
• Chapter 13. Generative adversarial networks in medical imaging
• Chapter 14. Deep learning from small labeled datasets applied to medical image analysis
• Index