Subhash Mondal

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



Research Interests: Computer Vision, Computational Learning Theory, Artificial Intelligence


Subhash Mondal, MIEEE, MACM, has obtained his M.Tech (CSE ’07) and B.Tech (’05) form University of Calcutta, IN. Presently he is serving as an Assistant Professor in the Department of Computer Science and Engineering, Meghnad Saha Institute of Technology, Kolkata, IN. He is a member of ACM and is serving in the capacity of Faculty Sponsor for the ACM Student Chapter. He is conducting his doctoral studies in Artificial Intelligence and its Scope in Ophthalmology. His research interests concentrate primarily on Computer Vision, Bio-Informatics, Deep Learning Frameworks, and Internet of Things allied to Artificial Intelligence. Recently he has also scored publications in houses such as Springer Nature, and AGH University of Science and Technology, Krakow Poland. He has proven himself as a successful administrator in roles such as the Training and Placement depute from the department, PG Coordinator, convener for the Institutional Professional Membership Coordination Committee, and member of the Disciplinary committee, to name a few apart from successfully convening multiple institutional events under the banner of IEEE, and ACM.

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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