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Application of Deep Learning Methods in Healthcare and Medical Science



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ISBN 9781774911204
January 12, 2023 Forthcoming by Apple Academic Press
304 Pages 111 B/W Illustrations

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Book Description

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.

Table of Contents

1. Review of Detection Analysis to Find Kidney Abnormalities from Various Images Using Machine and Deep Learning Techniques

Vemu Santhi Sri, P. Sathish Kumar, and V. Rajendran

2. Deep Learning-Based Computer-Aided Diagnosis System

G. Vijaya

3. Extensive Study of WBC Segmentation Using Traditional and Deep Learning Methods

Chandradeep Bhatt, Indrajeet Kumar, Sandeep Chand Kumain, and Jitendra Kumar Gupta

4. Introduction and Application of SVM in Brain Tumor Segmentation

Amit Verma

5. Detection Analysis of Covid-19 Infection Using the Merits of Lung CT Scan Images with Pre-Trained VGG-16 and 3-Layer CNN Models

P. Vijayalakshmi, P. Sathish Kumar, and V. Rajendran

6. Deep Learning Methods for Diabetic Retinopathy Detection

Tahir Javed, Sheema Parwaz, and Janibul Bashir

7. Study to Distinguish Covid-19 from Normal Cases Using Chest X-Ray Images with Convolution Neural Network

P. Sathish Kumar, P. Vijayalakshmi, and V. Rajendran

8. Breast Cancer Classification Using CNN Extracted Features: A Comprehensive Review

Arpit Kumar Sharma, Amita Nandal, Todor Ganchev, and Arvind Dhaka

9. Multimodal Image Fusion with Segmentation for Detection of Brain Tumor Using Deep Learning Algorithm

M. Padma Usha and G. Kannan

10. Unrolling the Covid-19 Diagnostic Systems Driven by Deep Learning

Sakshi Aggarwal, Navjot Singh, and K. K. Mishra

11. Generative Model and Its Application in Brain Tumor Segmentation

Amit Verma

12. Genomic Sequence Similarity of SARS-CoV2 Nucleotide Sequences Using Biopython: Key for Finding Cure and Vaccines

Sweeti Sah, B. Surendiran, and R. Dhanalakshmi

13. Autonomous Logistic Transportation System for Smart Healthcare System

Saswati Kumari Behera and G. Prashanth

14. Survey on Cancer Diagnosis from Different Tests and Detection Methods with Machine and Deep Learning

Ghost Manoj Kumar, P. Sathish Kumar, and V. Rajendran

15. A Deep Learning Based Portable Digital X-Ray Devices for Covid-19 Patients

Gopal Sakarkar, Vivek Tiwari, Rama Rao Karri, and Siti Sophiayati Yuhaniz

16. Adoption of Machine Learning and Open Source: Healthcare 4.0 Use Cases

Neelu Jyothi Ahuja

...
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Editor(s)

Biography

Rohit Tanwar, PhD, is Assistant Professor at the School of Computer Sciences, University of Petroleum and Energy Studies, Dehradun, India. He has more than 10 years of experience in teaching. His areas of interests include network security, optimization techniques, human computing, soft computing, cloud computing, data mining, etc. Dr. Tanwar has published one book, Information Security and Optimization, and three others are in progress with reputed international publishers. He is associated with some highly indexed international journals as guest editor/onboard reviewer. He has more than 30 publications to his credit to date in reputed journals and conferences. He has been associated with many conferences throughout India as member, session chair, etc. He is currently supervising two PhD research scholars in the fields of security and optimization. Dr. Tanwar received his bachelor’s degree in CSE from Kurukshetra University, Kurukshetra, India, and master’s degree in CSE from YMCA University of Science and Technology, Faridabad, India. He has received his PhD in CSE from Kurukshetra University, India.

Prashant Kumar, PhD, is Assistant Professor in the Department of Computer Science and Engineering at the Dr. BR Ambedkar National Institute of Technology, Jalandhar, India. Previously he has worked with the Department of Systemics in the School of Computer Science at the University of Petroleum and Energy Studies, Dehradun, India, and the Department of Computer Science and Engineering at the National Institute of Technology Hamirpur, India. His research interests include opportunistic and delay-tolerant networks, device-to-device communications, wireless and adhoc networks, and security in wireless networks. He has published more than 25 research papers in journals and conferences of international repute. He is a member of IEEE, International Association of Engineers, and the Internet Society. Dr. Kumar received his PhD and MTech degrees from the National Institute of Technology Hamirpur, India.

Malay Kumar, PhD, is Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Information Technology Dharwad, India. Earlier he was associated with the School of Computer Science of the University of Petroleum and Energy Studies, Dehradun, India. He has authored more than 20 research papers in international journals and conferences. His research areas of interest are application of machine learning and deep learning in medical sciences, and security and privacy issues in cloud computing. He has served as chair and technical program committee member for numerous international conferences and workshops. He was a guest editor of several international journals and a lead editor of several books. Dr. Kumar earned his BTech in Computer Science and Engineering from CSJM University Kanpur, his MTech from NIT Kurukshetra, and his PhD from NIT Raipur, India

Neha Nandal, PhD, is Assistant Professor in the Computer Science and Engineering Department of the Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India. She has published 19 articles in her research area in different journals and conferences, including SCI- and SCOPUS-level journals. She is a life-time member of IETA. Dr Neha has participated in different workshops; completed courses on Python, machine learning, and deep learning on Coursera; and also hosted different faculty development programs. Her research interests include pattern recognition and machine learning. She earned her BTech in CSE from the Technological Institute of Textile and Sciences, Bhiwani, and her MTech in CSE from Amity University Jaipur, India, with distinction. Recently, she has been awarded a PhD in Machine Learning.