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 Book Chapter Faculty Publications- Book Chapter 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 Jenefa, J.; Vetriveeran, Divya; Sambandam, Rakoth Kandan; Vinodha, D.; Thaiyalnayaki, S.; Karthikeyan, P. Title A name given to the resource A Comparative Analysis of AlexNet and ResNet for Pneumonia Detection 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 Environmental Monitoring Using Artificial Intelligence;pp.225-250 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1002/9781394270392.ch11" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9781394270392.ch11</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105001346684?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105001346684?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 Jenefa J., CHRIST (Deemed to be University), Bangalore, India; Vetriveeran D., CHRIST (Deemed to be University), Bangalore, India; Sambandam R.K., CHRIST (Deemed to be University), Bangalore, India; Vinodha D., CHRIST (Deemed to be University), Bangalore, India; Thaiyalnayaki S., Bharath Institute of Higher Education and Research, Chennai, India; Karthikeyan P., National Chung Cheng University, Chiayi, Taiwan Description An account of the resource Pneumonia damages the lungs and results in swelling and fluid build-up in the air sacs, a major problem that can be assessed using AI techniques. Rapid detection is very important for timely treatment and effective medical care. Chest X-ray imaging stands out as a forefront diagnostic modality for pneumonia, owing to its non-invasive characteristics, cost-effectiveness, and ubiquitous accessibility across many medical facilities. A large volume of data, which can be used to generate medical intelligence, is being collected daily. In recent years, CNNs have exhibited exceptional efficacy in diverse medical image-processing endeavors; X-ray images of the chest play a pivotal role, particularly in diagnosing pneumonia. In CNN architectures, AlexNet and ResNet emerge as the frequently utilized and effective models for various medical problems and diagnoses. As a result, our research involves a comparative analysis of how well AlexNet and ResNet perform in identifying pneumonia. According to our experimental results, ResNet outperforms AlexNet regarding effective classification parameters. 2025 Scrivener Publishing LLC. Subject The topic of the resource AlexNet; CNN; pneumonia detection; ResNet Publisher An entity responsible for making the resource available wiley Relation A related resource ISBN: 978-139427039-2; 978-139427036-1; Language A language of the resource English Type The nature or genre of the resource Book chapter 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