Biomedical Engineering/Nanotechnology

Applied Intelligence for Medical Image Analysis
Editors: Aarti, PhD
Raju Pal, PhD
Mukesh Saraswat
Himanshu Mittal, PhD

Applied Intelligence for Medical Image Analysis

Published. Available now.
Pub Date: July 2024
Hardback Price: see ordering info
Hard ISBN: 9781774914762
E-Book ISBN: 9781003461852
Pages: 272pp w/index
Binding Type: Hardback / eBook
Notes: 11 color, 68 b/w illustrations

Over the last decades, there has been a revolution in the analysis of medical images with the new technological advancements. Innovative intelligent technologies have been developed to analyze and interpret medical images automatically for diseases diagnosis and disease assessment and for combatting new diseases.

This new volume explores the latest cutting-edge research in medical image analysis. The advanced intelligent technologies discussed include machine learning, ensemble methods in machine learning, deep learning methods and firebase technology, infrared thermography, deep convolution neural networks, and more. Some of the specific uses of these technologies include for brain tumor MRIs, for breast cancer screening, for polycystic ovary syndrome classification, for detecting and monitoring Alzheimer’s disease, for monitoring of newborns, for retinal disease diagnosis, for Covid-19 detection, and more.

Key features

  • Covers the application of deep learning techniques for the medical image problems
  • Considers the development of future machine learning techniques and further applications of existing techniques
  • Presents literature reviews on deep learning methods for medical image analysis
  • Discusses the applications of applied intelligence for the diagnosis of medical images, image genomics, and brain connectomics
  • Relays the latest technologies and algorithms related to the state-of-the-art medical image analysis in assessing and diagnosing diseases
Applied Intelligence for Medical Image Analysis will be of interest to academics and researchers in the area of deep learning and medical imaging.

CONTENTS:
Preface

Introduction

1. A Comparative Study of Anisotropic Diffusion Filters for Medical Image Denoising
Amira Hadj Fredj, Jihene Malek, and Souhir Mabrouk

2. Salt and Pepper Noise Removal Techniques for Medical Image Reconstruction
Vatsal Nanda, Prateek Jeet Singh Sohi, Bharat Garg, and Prashant Singh Rana

3. Comparative Analysis of PSP- and WOA-Based Segmentation of Brain Tumor MRIs
Reena Tripathi and Bindu Verma

4. Breast Cancer Screening Using Fractal Dimension of Chromatin in Interphase Nuclei of Buccal Epithelium
Dmitriy Klyushin, Kateryna Golubeva, Natalia Boroday, and Chan Kha Vu

5. Polycystic Ovary Syndrome Classification Based on Machine Learning
Nancy Girdhar, Priya Singh, and Tisha Singhal

6. A Comprehensive Review on Diagnosis of Alzheimer’s Disease Using Ensemble Methods and Machine Learning
Prachi Patil and Sujata Kadu

7. A New Strategy for Prediction of Diabetic Retinopathy Using Deep Learning Methods and Firebase Technology
Abdelwaheb Jebnouni, Amira Hadj Fredj, and Jihene Malek

8. Contactless Monitoring in Newborns Using Infrared Thermography: A Review
Lalit Maurya, Roop Singh, Deepak Chawla, and Prasant Mahapatra

9. Retinal Disease Diagnosis Using Machine Learning Techniques
G. Kalaiarasi, B. Saritha, S. K. Kabilesh, and D. Mohanapriya

10. Automated Segregation of Lymphoid and Myeloid Blasts in Acute Leukemia Cases Using a Deep Convolutional Neural Network
Anilkumar K. K., Manoj V. J, and Sagi T. M

11. Evaluation of Deep Learning Network Architectures for Medicine Expenditure Prediction in the Healthcare Domain
Ulises Manuel Ramirez-Alcocer, Jaciel David Hernandez-Resendiz, and Edgar Tello Leal

12. Covid-19 Detection from Chest X-Ray Using a Customized Artificial Neural Network
Vinayak Tiwari, Amit Singhal, and Nischay Dhankhar

13. An Automated Deep Learning Approach to Classify ECG signals using AlexNet
Neelofer Shaheen, Mudassir Hasan Khan and Mohammad Sarfraz

14. MLO and CC View of Feature Fusion and Mammogram Classification Using a Deep Convolution Neural Network
V. Sridevi and J. Abdul Samath

Index


About the Authors / Editors:
Editors: Aarti, PhD
Associate Professor, Department of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India

Dr. Aarti is working as an Associate Professor in the Computer Science and Engineering Department at Lovely Professional University, Phagwara, India. She has more than seven years of experience in teaching. She is currently working on optimization of nature-inspired algorithms for the medical field. She is passionate in her work in data mining, machine learning, and optimization of learning techniques medical images and fault-tolerance. She has published more than 20 papers in the field of mining, security, and medical image analysis. She is reviewer of many journals. She earned her PhD and MTech from Dr. B. R. Ambedkar National Institute of Technology Jalandhar, India.

Raju Pal, PhD
Assistant Professor, Department of Computer Science and Engineering, School of Information and Communication Technology, Gautam Buddha University, Greater Noida, India

Raju Pal, PhD, has more than eleven years of teaching and research experience. He is currently Assistant Professor in the Department of Computer Science and Engineering at the School of Information and Communication Technology at Gautam Buddha University, Greater Noida, India. He was formerly a faculty member in the Department of Computer Science and Engineering at Jaypee Institute of Information Technology, Noida, India, where he earned his doctorate degree in Medical Images Analysis. He is passionate in the area of machine learning, medical image analysis, and wireless sensor networks. He has made substantial contributions to the field of image processing and machine learning with many published research articles of high repute. He was part of the successfully completed SERB-DST (New Delhi) funded project on Histopathological Image Analysis. He is the reviewer of many international journals, including the Journal of Communications and Networks, Future Generation Computer Systems, Neural Computing and Applications, etc.

Mukesh Saraswat
Associate Professor, Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, India

Dr. Mukesh Saraswat has more than 18 years of teaching and research experience. He has guided PhD students and over 50 MTech and BTech in their dissertations. He has published more than 40 journal and conference papers in image processing, pattern recognition, data mining, and soft computing. He is the part of successfully completed DRDE-funded project on image analysis and is currently running two projects funded by SERB-DST (New Delhi) on Histopathological Image Analysis and Collaborative Research Scheme (CRS), under TEQIP III (RTU-ATU) on Smile. He has been an active member of many organizing committees of various conferences and workshops. He was also a guest editor of the International Journal of Swarm Intelligence. He is an active member of IEEE, ACM, and CSI professional bodies. His research areas include image processing, pattern recognition, data mining, and soft computing.

Himanshu Mittal, PhD
Associate Professor, Department of Artificial Intelligence and Data Sciences, Indira Gandhi Delhi Technical University for Women, Delhi, India

Dr. Himanshu Mittal has more than eleven years of teaching and research experience. He is currently Associate Professor in the Department of Artificial Intelligence and Data Sciences at Indira Gandhi Delhi Technical University for Women, Delhi, India. He was formerly a faculty member in the Department of Computer Science and Engineering at Jaypee Institute of Information Technology, Noida, India, where he earned his doctorate degree. His interest areas include deep learning, machine learning, medical image analysis, and soft computing. He has published research publications in the field of image analysis. He is one of members of the successfully completed SERB-DST funded project on Histopathological Image Analysis. He is the reviewer of many international journals, including Future Generation Computer Systems, International Journal of Machine Learning and Cybernetics, etc.




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