Suharta Banerjee

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



Research Interests: Computer Networks, Computer Vision, Computational Learning Theory


Suharta Banerjee is presently pursuing his B.Tech (2022) in Computer Science and Engineering from Meghnad Saha Institute of Technology, Kolkata, India. He is a member of ACM. He has served as the Chair of the Student Chapter for MSIT ACM Student Chapter. His research interest includes Machine Learning, Computer Vision, Deep Learning, and Bio-Informatics. He has published one conference paper in EAIT 2021 (Springer) which is a part of Lecture Notes in Networks and Systems (LNNS) book series. Recently one more credible work has been accepted for publication in the ESCI, SCOPUS indexed journal Computer Science.

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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