Dr. Nilanjan Dey
- Asst. Professor, Dept. of IT, Techno India College of Technology, India. Visiting Fellow, WC Laboratory, Department
of Biomedical Engineering, University of Reading, UK, Ambassador - IFIP InterYIT, India.
Specialization: Medical Imaging, Machine learning, Computer Aided Diagnosis, Data Mining
Nilanjan Dey is an Assistant Professor in Department of Information Technology at Techno India College of Technology, Kolkata, India. He is a visiting fellow of the Biomedical Engineering, School of Biological Sciences,
The University of Reading, UK.. He is a Visiting Professor at Duy Tan University, Vietnam; He was an honorary Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012-2015). He was awarded his PhD. from Jadavpur
University in 2015.
He has authored/edited more than 75 books with Elsevier, Wiley, CRC Press and Springer, and published more than 300 papers (H-Index 41). He is the Editor-in-Chief of International Journal of Ambient
Computing and Intelligence, IGI Global (Scopus Q2, ESCI), Associated Editor of IEEE Access and International Journal of Information Technology, Springer. He is the Series Co-Editor of Springer Tracts in Nature-Inspired Computing, Springer, Series
Co-Editor of Advances in Ubiquitous Sensing Applications for Healthcare, Elsevier, Series Editor of Computational Intelligence in Engineering Problem Solving and Intelligent Signal processing and data analysis, CRC.
He is the Indian Ambassador of International Federation for Information Processing – Young ICT Group. He is among the top 5 most published academics in the field of Computer Science in India (2014-19) [Sci_Val Scopus].
Computer-aided Detection and Diagnosis in Medical Imaging
Abstract: Advancement in medical imaging modalities results in huge varieties of images engaged in different management phases, namely prognosis,
diagnosis, and treatment. In clinical practice, imaging has reserved a vital role to assist physicians and medical experts in decision-making. However, the counterpart that the physician faces is the complexity to deal with a large amount of data and image
contents. Mainly, the interpretation is based on the physician’s observations, which is tedious, subject to error, and highly dependent on the skills and experience of the clinicians. Accordingly, emerging demand for automated tools become essential
for detecting, quantifying, and classifying the disease for an accurate diagnosis. Computer-aided diagnosis (CAD) is an emergent research area that aims to meet the physicians’ demands, to speed up the diagnostic process, to reduce diagnostic errors,
and to improve the quantitative evaluation. It is primarily based on medical images that provide direct visualization of the bodies and information ranging from functional activities, anatomical information, to the cellular and molecular expressions.
This talk provides a state-of-the-art sight in medical imaging applied to CAD. It highlights the different imaging modalities, such as Magnetic Resonance Imaging, Computed Tomography, Positron Emission Tomography, and Ultrasound.
The talk emphasizes on the ability of CAD to improve the diagnostic accuracy and different future directions as an opening that gathers the clinicians and engineers for an accurate diagnosis.