This volume provides a wealth of up-to-date information on developments and applications of deep learning in healthcare and medicine. It aims to provide 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 a logistic and transport systems for effective delivery of healthcare.
Chapters include studies and discussions on chest X-ray images using CNN to identify Covid-19 infections, lung CT scan images using pre-trained VGG-16 and 3-layer CNN to distinguish Covid and non-Covid patients, genomic sequencing to study the Covid virus, breast cancer identification using CNN, brain tumor detection using multimodal image fusion and segmentation, factors responsible for birth asphyxia in neonates, and much more. It also explores cancer identification and detection using deep learning methods in the human body through algorithms based on issues, laboratory tests, imaging tests, biopsies, bone scans, computerized tomography scans, positron emission tomography, and ultrasound.
This volume, Application of Deep Learning Methods in Healthcare and Medical Science, showcases the diverse applications of patient-based data collection and analysis in medicine and healthcare using computer algorithms for effective health diagnosis, prevention, and patient care.
