Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Roy, Santanu; Suresh, Ashvath; Sahu, Palak; Roy, Achintya; Gupta, Tulika Rudra Title A name given to the resource Novel Pooling-Based VGG-Lite for Pneumonia and Covid-19 Detection From Imbalanced Chest X-Ray Datasets Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived IEEE Transactions on Emerging Topics in Computational Intelligence;Volume;9;Issue;6;pp.4231-4242 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/TETCI.2025.3577509" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TETCI.2025.3577509</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105008183849?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105008183849?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Roy S., Department of Computer Science and Engineering, Christ (Deemed to be University) Bangalore, Bangalore, 560029, India, Manipal Academy of Higher Education (MAHE), Manipal, 576104, India; Suresh A., NIIT University, Department of Computer Science and Engineering, Jaipur, 301705, India; Sahu P., National Institute of Technology Warangal, Department of Mathematics, Hanamkonda, 506004, India; Roy A., Indian Institute of Technology, Kharagpur, 721302, India, Harvard Medical School, Dana-Farber Cancer Institute, Boston, 02115, MA, United States; Gupta T.R., Harvard Medical School, Dana-Farber Cancer Institute, Boston, 02115, MA, United States Description An account of the resource This paper proposes a novel pooling-based VGG-Lite model in order to mitigate class imbalance issues in Chest X-Ray (CXR) datasets. Automatic Pneumonia detection from CXR images by deep learning model has emerged as a prominent and dynamic area of research, since the inception of the new Covid-19 variant in 2020. However, the standard Convolutional Neural Network (CNN) models encounter challenges associated with class imbalance, a prevalent issue found in many medical datasets. The innovations introduced in the proposed model architecture include: (I) A very lightweight CNN model, VGG-Lite, is proposed as a base model, inspired by VGG-16 and MobileNet-V2 architecture. (II) On top of this base model, we leverage an Edge Enhanced Module (EEM) through a parallel branch, consisting of a negative image layer, and a novel custom pooling layer 2Max-Min Pooling. This 2Max-Min Pooling layer is entirely novel in this investigation, providing more attention to edge components within pneumonia CXR images. Thus, it works as an efficient spatial attention module (SAM). We have implemented the proposed framework on two separate CXR datasets. The first dataset is obtained from a readily available source on the internet, and the second dataset is a more challenging CXR dataset, assembled by our research team from three different sources. Experimental results reveal that our proposed framework has outperformed pre-trained CNN models, and three recent trend existing models Vision Transformer, Pooling-based Vision Transformer (PiT) and PneuNet, by substantial margins on both datasets. The proposed framework VGG-Lite with EEM, has achieved a macro average of 95% accuracy, 97.1% precision, 96.1% recall, and 96.6% F1 score on the Pneumonia Imbalance CXR dataset, without employing any pre-processing technique. 2017 IEEE. Subject The topic of the resource chest X-ray (CXR) images; class-imbalance problem; Complementary and edge enhanced module (CEEM); novel pooling technique; pneumonia and COVID-19 detection; spatial attention module (SAM); vision transformer (ViT) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISSN: 2471285X; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online