1st Edition
Applied Intelligence for Medical Image Analysis
Over the last decades, there has been a revolution in the use of new intelligent technologies to analyze and interpret medical images for diseases diagnosis, assessment ad treatment. 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.
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
Biography
Aarti, PhD, is an Associate Professor in the Computer Science and Engineering Department at Lovely Professional University, Phagwara, India. She is currently working on optimization of nature-inspired algorithms for the medical field, along with data mining, machine learning, and optimization of learning techniques medical images and fault-tolerance. She has published papers in the field of mining, security, and medical image analysis and is a reviewer for several journals.
Raju Pal, PhD, is an Assistant Professor of Computer Science and Engineering at the School of Information and Communication Technology at Gautam Buddha University, Greater Noida, India. He was formerly affiliated with the Jaypee Institute of Information Technology, Noida, India. His 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. He was part of a successfully completed SERB-DST (New Delhi) funded project on Histopathological Image Analysis. He is a reviewer for many international journals, including the Journal of Communications and Networks, Future Generation Computer Systems, Neural Computing and Applications, etc.
Mukesh Saraswat, PhD, is an Associate Professor of Computer Science and Engineering at Jaypee Institute of Information Technology, Noida, India. He has more than 18 years of teaching and research experience, during which he has guided many PhD, MTech and BTech students. He has published journal and conference papers on image processing, pattern recognition, data mining, and soft computing, and also guest edited the International Journal of Swarm Intelligence. He is a part of several funded projects on histopathological image analysis.
Himanshu Mittal, PhD, is an Associate Professor of Artificial Intelligence and Data Science at Indira Gandhi Delhi Technical University for Women, Delhi, India. He was formerly a faculty member at Jaypee Institute of Information Technology, Noida, India. 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 the members of the successfully completed SERB-DST funded project on Histopathological Image Analysis. He is a reviewer for many international journals, including Future Generation Computer Systems, International Journal of Machine Learning and Cybernetics, etc.
