State of the Art in Neural Networks and Their Applications

Dr. Jasjit Suri, PhD, MBA, is a renowned innovator and scientist. He received the Director General’s Gold Medal in 1980 and is a Fellow of several prestigious organizations, including the American Institute of Medical and Biological Engineering and the Institute of Electrical and Electronics Engineers. Dr. Suri has been honored with lifetime achievement awards from Marcus, NJ, USA, and Graphics Era University, India. He has published nearly 300 peer-reviewed AI articles, 100 books, and holds 100 innovations/trademarks, achieving an H-index of nearly 100 with about 43,000 citations. Dr. Suri has served as chairman of AtheroPoint, IEEE Denver section, and as an advisory board member to various healthcare industries and universities globally.Dr. El-Baz is a Professor, University Scholar, and Chair of the Bioengineering Department at the University of Louisville, KY. Dr. El-Baz earned his bachelor's and master’s degrees in Electrical Engineering in 1997 and 2001, respectively. He earned his doctoral degree in electrical engineering from the University of Louisville in 2006. In 2009, Dr. El-Baz was named a Coulter Fellow for his contributions to the field of biomedical translational research. Dr. El-Baz has 15 years of hands-on experience in the fields of bio-imaging modeling and non-invasive computer-assisted diagnosis systems. He has authored or coauthored more than 450 technical articles (105 journals, 15 books, 50 book chapters, 175 refereed-conference papers, 100 abstracts, and 15 US patents).

• 1. Microscopy Cancer Cell Imaging in B-Lineage Acute Lymphoblastic Leukemia2. Computational Applications in Brain and Breast Cancer 3. Deep Neural Networks and Advanced Computer Vision Algorithms in The Early Diagnosis of Skin Diseases4. An Accurate Deep Learning-Based CAD System For Early Diagnosis Of Prostate Cancer5. Adaptive Graph Convolutional Neural Network and its Biomedical Applications6. Deep Slice Interpolation via Marginal Super-Resolution, Fusion and Refinement7. New Explainable Deep CNN Design for Classifying Breast Tumor Response over Neoadjuvant Chemotherapy  8. Deep Learning Interpretability: Measuring The Relevance of Clinical Concepts in CNN Features9. Computational Lung Sound Classification: A Review10. Clinical Applications of Machine Learning in Heart Failure11. Role of AI and Radiomics in Diagnosing Renal Tumors: A Survey12. Texture-Centric Diagnostic Models for Thyroid-Cancer Using Convolutional Neural Networks: Bridging the Gap Between Radiomics and Microscopic Domains