Ankur Ganguly

Work place: Meghnad Saha Institute of Technology, Kolkata, India



Research Interests: Computational Engineering, Computational Science and Engineering


Dr. Ankur Ganguly is presently the Principal of Meghnad Saha Institute of Technology since 1st July 2020. He received his Bachelor’s Degree in Electrical & Electronics Engineering from Mangalore University, Master’s degree from MAHE Deemed University and PhD in Engineering from Jadavpur University. He worked in the industry for a couple of years and joined the Academic Fraternity as Lecturer in Electronics & Communication Engineering in 2002. He was the Head of the Department of Biomedical Engineering, Electronics & Instrumentation Engineering in different academic institutes before he took the responsibility of Principal in 2013. He has been appointed External Examiner of different Universities since 2004. He is also the Member & Convener of Board of Studies of Maulana Abul Kalam Azad University of Technology, West Bengal along with his regular assignments. He is also associated with various Governmental bodies in different capabilities. His main research interests are in the areas of Power Quality, Renewable Energy, Biomedical Signal Processing, Heart Rate Variability, etc. He has published widely in international journals and conferences. He has participated in many conferences in the capacity of PC member, invited speaker, etc. He was the Programme Committee Co-chair of various International Conferences. He is also on the editorial board of many international journals. Prof. Ganguly has multiple patents to his credit. He has guided 2 Ph.D. scholars and another two scholars are registered and in the process of completing their PhD degrees. Prof. Ganguly is the Fellow of Institute of Electronics & Telecommunication Engineers (India), Fellow of Institute of Engineers (India), Life Member of Indian Society for Technical Education, Senior Member of IEEE (USA), Life Member - Indian Society of Biomechanics (India) and Member of Computer Society of India.

Author Articles
Deep Classifier for Conjunctivitis – A Three-Fold Binary Approach

By Subhash Mondal Suharta Banerjee Subinoy Mukherjee Ankur Ganguly Diganta Sengupta

DOI:, Pub. Date: 8 Jun. 2022

Alterations in environmental and demographic equations have resulted in phenomenal rise of human centric diseases, ocular being one of them. Technological advancements have witnessed early diagnosis of much of the previously un-ciphered diseases. This paper addresses two research questions (RQs) with the study being focused on conjunctivitis (the most prevalent eye ailment in adults as well as minors). The motive of both the RQs rests in implementing three state-of-the art deep learning framework for classification of the ocular disease and validation of the frameworks. Validation of the frameworks is seconded by improvised proposals for enhancements. RQ1 establishes and validates whether the three off the shelf Deep Learning frameworks VGG19, ResNet50, and Inception V3 properly classify the disease or not. RQ2 analyses the effectiveness of each classifier with further enhancement proposals. The algorithms were implemented on 210 images and generated an accuracy of 87.3%, 93.6%, and 95.2% for VGG19, ResNet50, and Inception V3 using Adam optimizer, with slightly variant results when applying Adadelta optimizer. These results were typical of the classification frameworks with enhancements. With pervasive penetration of Artificial Intelligence in healthcare, this paper presents the efficacy of Deep Learning Frameworks in conjunctivitis classification.

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