Diganta Sengupta

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

E-mail: sg.diganta@ieee.org


Research Interests: Quantum Computing Theory, Processor Design, Computer Architecture and Organization, Computer Vision, Computational Learning Theory, Computer systems and computational processes


Diganta Sengupta Ph.D. SMIEEE (M’16), SMACM (M'17), Life member of Computer Society of India (LM’18) and The Institution of Engineers (India) (M’16). He is also a member of IEEE Computational Intelligence Society. He has served as the State Student Coordinator for West Bengal, India for Computer Society of India. He received his B.Tech (2004) from University of Kalyani, WB, IN, his M.Tech (2010), and Ph.D. (Engg.) (2016) from Jadavpur University, WB, IN. Dr. Diganta Sengupta is presently working in the capacity of Associate Professor in the Dept. of CSE, and Faculty-In-Charge for the Dept. of Computer Science and Business Systems (additional responsibility), Meghnad Saha Institute of Technology, Kolkata, India. Formerly he was associated with Techno International Batanagar, Kolkata, India, the School of Computer Engineering (SCOPE), VIT University, Vellore, India and also with Future Institute of Engineering and Management, Kolkata, IN. His research interests include Computer Vision, Bio-Informatics, Deep Learning, Quantum Computing, Processor Architecture, and Taxonomy Generation Process. He has served as a reviewer for IEEE Access, JIKM (World Scientific), IJSAEM (Springer), IJBDCN (IGI-Global), ISSE (Springer), SIMPAT (Elsevier), Complexity (Hindawi), ISA Transactions, IET Journal of Engineering, IJEOE (IGI-Global) to name a few. He has also been associated with reputed international IEEE and Springer conferences in multiple committee member capacities, few of which are IEEE DCS VLSI 2022, IACC 2021 Springer, DICTA 2021 IEEE, ICRITO 2021IEEE, I2AS 2021 Springer, CICBA 2021 Springer, EAIT 2020 Springer, ICRITO 2020 IEEE, ICCSEA 2020 IEEE, ISCON 2019 IEEE, AICAI 2019 IEEE, ICRITO 2018 IEEE, ICRCICN 2017 IEEE, CSNT 2016 IEEE. He is currently serving as the Lead Guest Editor in a Special Issue titled “Reversible Quantum Communication & Systems” in IET Quantum Communication.

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

By Subhash Mondal Suharta Banerjee Subinoy Mukherjee Ankur Ganguly Diganta Sengupta

DOI: https://doi.org/10.5815/ijmsc.2022.02.05, 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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