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Advances in Healthcare Using Machine Learning by Sriparna Saha (Hardback)
£130.00
Available
Product Ref. (e.g. ISBN, SKU)
9781032853482
Published:
30 Dec 2025
Format:
Hardback
Number of pages:
264 pages
Dimensions:
234mm
Weight:
0.453kgs
Author:
Advancements in AI and Computer Vision are revolutionizing medical diagnostics by enabling real-time analysis of vast data. This book focuses on ML algorithms that analyze medical data and predict diseases based on key features.
There have been very significant technological advancements made in the healthcare industry in recent decades. They have not only helped us better comprehend the morphology and physiology of the various organs that make up the human body but have also advanced the diagnosis and, consequently, the treatment of several diseases in a variety of medical specialties from very early stages. The advancements in artificial intelligence (AI) and computer vision (CV) have primarily made this possible. In a nutshell, these tools enable us to collect, process, interpret, and analyze a limitless quantity of static and dynamic medical data in real-time, which will improve the way each disease is characterized and the patients that are chosen. Many potentially fatal illnesses, such as COVID-19, pneumonia, and cancer, can be cured if diagnosed in the initial stages very early on. Computer-based medical imaging techniques, such as CT scans and X-rays, can be 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. Application of machine learning can make the predictions of these diseases more accurate. and help the clinician to detect appropriate ones. This helps in faster recognition of disease as well as with the intervention of the technology, makes it feasible to spread to remote places. The goal is to create machine learning algorithms that aid in the analysis of diverse medical data and the prediction of diseases based on the characteristics of the data.
About the Author: Dr Sriparna Saha is in Maulana Abul Kalam Azad University of Technology, West Bengal. She has her Ph.D. in 'A Machine Intelligence Approach to Gesture Recognition' guided by Prof. Amit Konar, Electronics and Telecommunication Engineering Department, Jadavpur University in 2018. Dr. Lidia Ghosh is a committed academician with knowledge of cutting edge technologies in Cognitive Neuroscience. She is currently an Assistant Professor in the department of Computer Application in RCC Institute of Information Technology, Kolkata, India. Prior to joining RCCIIT, Kolkata, she worked as Assistant Professor in the CSE Department of IEM, Kolkata. She had the privilege of serving as a Postdoctoral Fellow at Liverpool Hope University, UK, under the auspices of the Global Challenges Research Funding Scheme. She was honored to be the 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. In recognition of her academic excellence, she was bestowed with the University Gold Medal for achieving the highest percentage of marks in M.Tech. at Jadavpur University in 2015. Furthermore, she had the opportunity to contribute as a guest faculty member at Jadavpur University throughout my tenure as a Ph.D. candidate and Postdoctoral Fellow. Her achievements were recognized through nominations for the esteemed Inspire Fellowship and Inspire Faculty Fellowship. She has made notable contributions to academia through her publication of a monograph, published by the IEEEWiley press, and approximately 50 research papers in esteemed international journals and conference proceedings. Her expertise has also been sought as a Reviewer for esteemed publications such as the IEEE Transactions on Fuzzy Systems and the IEEE Transactions on Emerging Topics in Computational Intelligence, among others.
| Number of pages | 264 |
|---|
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