Advances in Healthcare Using Machine Learning, Volume 2

The technological advancements made in recent decades have not only helped us better comprehend the morphology and physiology of the organs of the human body, but they have also advanced the diagnosis and, therefore, the treatment of a number of diseases in a variety of medical specialties from very early stages. Artificial Intelligence (AI) and Computer Vision (CV) enable us to collect, process, interpret, and analyze a limitless quantity of static and dynamic medical data in real time, which improve the way each disease is characterized and the patients are chosen. Many potentially fatal illnesses, such as COVID-19, pneumonia, and cancer, can be cured if diagnosed in initial stages very early on. Computer-based medical imaging techniques, such as CT scan and X-rays are useful in detecting all of these illnesses. On the other hand, various brain anomalies and heart diseases can also be anticipated using biological signals, like electroencephalography (EEG), electrocardiogram (ECG) etc. The application of machine learning makes the predictions more accurate and help the clinician to detect appropriate one. This helps in faster recognition of disease as well as with the intervention of the technology, makes it feasible to spread to the remote places. The goal of the book is to create machine learning algorithms that aids in the analysis of diverse medical data and the prediction of diseases based on the characteristics of the data.

Sriparna Saha received her M.E. and Ph.D. degrees from Electronics and Tele-Communication Engineering department of Jadavpur University, Kolkata, India. She is currently an Assistant Professor (Stage-II) in the Department of Computer Science and Engineering of Maulana Abul Kalam Azad University of Technology, West Bengal, India. Prior to that, she was associated as a faculty with Jadavpur University and two other institutions. She has more than 12 years of experience in teaching and research. Her research area includes artificial intelligence, image processing, human computer interaction etc. She has over 80 publications in international journals and conference proceedings including IEEE, Elsevier, Springer, etc. She is the author of a book on Gesture Recognition published by Studies in Computational Intelligence, Springer, currently available in many online book retailers. She is also the reviewer for many international journals. Her major research proposal is accepted for Start Up Grant under UGC Basic Scientific Research Grant. Not only that, she also got University seed money for setting up her laboratory.

Lidia Ghosh is an Assistant Professor in the Department of Computer Application in RCC Institute of Information Technology (RCCIIT), Kolkata, India. Prior to joining RCCIIT, she served as Assistant Professor in IEM, Kolkata. She was a Postdoctoral Fellow at Liverpool Hope University, UK, under the auspices of the Global Challenges Research Funding Scheme. She is a recipient of the Rashtriya Uchchatara Shiksha Abhiyan Doctoral Fellowship and the University of Potential Excellence Junior Research Fellowship, both generously funded by the Ministry of Human Resource Development and the University Grant Commission of India, respectively. She has also received a University Gold Medal for achieving the highest percentage of marks in M. Tech. at Jadavpur University, India in 2015. She has been a guest faculty member at Jadavpur University throughout her tenure as a PhD candidate and Postdoctoral Fellow. She has been nominated for the Inspire Fellowship and Inspire Faculty Fellowship. She has made notable contributions to academia through her monograph and approximately 45 research papers in esteemed journals and conference proceedings. Her expertise has also been sought as a Reviewer for publications such as the IEEE Transactions on Fuzzy Systems and the IEEE Transactions on Emerging Topics in Computational Intelligence, among others. At present, her research interests encompass the fascinating fields of cognitive neuroscience, deep learning, type-2 fuzzy sets, human memory formation, interactions between short- and long-term memories, as well as the biological foundations of perception and scientific creativity.