Application of Deep Learning Methods in Healthcare and Medical Science

Rohit Tanwar, Prashant Kumar, Malay Kumar, Neha Nandal, Rohit Tanwar, Prashant Kumar, Malay Kumar, Neha Nandal

• Publisher: Apple Academic Press
• Year: 2022
• Print ISBN:
  9781774911211
• eBook ISBN:
  9781000610703
• Topic: Ciencia de la computación
• Subtopic: Desarrollo de software

The volume provides a wealth of up-to-date information on developments and applications of deep learning in healthcare and medicine, providing deep insight and understanding of novel applications that address the tough questions of disease diagnosis, prevention, and immunization. The volume looks at applications of deep learning for major medical challenges such as cancer detection and identification, birth asphyxia among neonates, kidney abnormalities, white blood cell segmentation, diabetic retinopathy detection, and Covid-19 diagnosis, prevention, and immunization. The volume discusses applications of deep learning in detection, diagnosis, intensive examination and evaluation, genomic sequencing, convolutional neural networks for image recognition and processing, and more for health issues such as kidney problems, brain tumors, lung damage, and breast cancer. The authors look at ML for brain tumor segmentation, in lung CT scans, in digital X-ray devices, and for logistic and transport systems for effective delivery of healthcare.

Table of contents

• Cover Page
• Half Title page
• Title Page
• Copyright Page
• About the Editors
• Contents
• Contributors
• Abbreviations
• Preface
• 1. A Review on Detection of Kidney Disease Using Machine Learning and Deep Learning Techniques
• 2. Deep Learning-Based Computer-Aided Diagnosis System
• 3. Extensive Study of WBC Segmentation Using Traditional and Deep Learning Methods
• 4. Introduction and Application of SVM in Brain Tumor Segmentation
• 5. Detection Analysis of COVID-19 Infection Using the Merits of Lungs CT Scan Images with Pre-Trained VGG-16 and 3-Layer CNN Models
• 6. Deep Learning Methods for Diabetic Retinopathy Detection
• 7. Study to Distinguish Covid-19 from Normal Cases Using Chest X-Ray Images with Convolution Neural Network
• 8. Breast Cancer Classification Using CNN Extracted Features: A Comprehensive Review
• 9. Multimodal Image Fusion with Segmentation for Detection of Brain Tumors Using a Deep Learning Algorithm
• 10. Unrolling the COVID-19 Diagnostic Systems Driven by Deep Learning
• 11. Generative Model and Its Application in Brain Tumor Segmentation
• 12. Genomic Sequence Similarity of SARS-CoV2 Nucleotide Sequences Using Biopython: Key for Finding Cure and Vaccines
• 13. Autonomous Logistic Transportation System for Smart Healthcare System
• 14. Survey on Cancer Diagnosis from Different Tests and Detection Methods with Machine and Deep Learning
• 15. A Deep Learning-Based Portable Digital X-Ray Devices for COVID-19 Patients
• 16. Adoption of Machine Learning and Open Source: Healthcare 4.0 Use Cases
• Index