A Comparative Analysis of AlexNet and ResNet for Pneumonia Detection
AlexNet; CNN; pneumonia detection; ResNet
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.
Jenefa, J.; Vetriveeran, Divya; Sambandam, Rakoth Kandan; Vinodha, D.; Thaiyalnayaki, S.; Karthikeyan, P.
Environmental Monitoring Using Artificial Intelligence;pp.225-250
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1002/9781394270392.ch11 https://www.scopus.com/pages/publications/105001346684?origin=resultslist
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