Applied Intelligence for Medical Image Analysis

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, 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. Himanshu Mittal, PhD, has more than eleven years of teaching and research experience. He is currently Associate Professor in the Department of Artificial Intelligence and Data Science 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. 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.

1. A Comparative Study of Anisotropic Diffusion Filters for Medical Image Denoising 2. Salt and Pepper Noise Removal Techniques for Medical Image Reconstruction 3. Comparative Analysis of PSP- and WOA-Based Segmentation of Brain Tumor MRIs 4. Breast Cancer Screening Using Fractal Dimension of Chromatin in Interphase Nuclei of Buccal Epithelium 5. Polycystic Ovary Syndrome Classification Based on Machine Learning 6. A Comprehensive Review on Diagnosis of Alzheimer’s Disease Using Ensemble Methods and Machine Learning 7. A New Strategy for Prediction of Diabetic Retinopathy Using Deep Learning Methods and Firebase Technology 8. Contactless Monitoring in Newborns Using Infrared Thermography: A Review 9. Retinal Disease Diagnosis Using Machine Learning Techniques 10. Automated Segregation of Lymphoid and Myeloid Blasts in Acute Leukemia Cases Using a Deep Convolutional Neural Network 11. Evaluation of Deep Learning Network Architectures for Medicine Expenditure Prediction in the Healthcare Domain 12. Covid-19 Detection from Chest X-Ray Using a Customized Artificial Neural Network 13. An Automated Deep Learning Approach to Classify ECG signals using AlexNet 14. MLO and CC View of Feature Fusion and Mammogram Classification Using a Deep Convolution Neural Network