International Journal on Science and Technology

E-ISSN: 2229-7677   •   Impact Factor: 9.88

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 16 Issue 2 April-June 2025 Submit your research before last 3 days of June to publish your research paper in the issue of April-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 16 Issue 2
2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

A Research of Pneumonia Detection Using EffcientNetV2L with Grad-Cam

Author(s) Sonadeepthi Meka, Mohana Priya Bonagiri, Venkata Vamsi Krishna Dusanapudi, Yeswanth Ram Kamavarapu, T Uday
Country India
Abstract The application of EfficientNetV2-L, an advanced deep learning model, for detecting pneumonia from chest X-ray images. Traditional diagnostic techniques, such as chest X-rays and lung biopsies, require skilled medical practitioners and can be time-consuming and error-prone, especially in resource-limited settings.By training EfficientNetV2-L on a dataset of chest X-ray images, the study classifies scans as either "Pneumonia" or "Normal." To enhance explainability, the model integrates Grad-CAM (Gradient-weighted Class Activation Mapping), which generates heatmaps highlighting critical areas influencing the model’s predictions. These visual explanations help clinicians understand the model's decision-making process.The proposed model achieved an impressive 94.02% accuracy, outperforming widely used architectures like CNN, ResNet50, and VGG16. Grad-CAM visualizations confirmed that the model effectively identifies key pneumonia indicators, such as consolidation and infiltration. The study concludes that combining EfficientNetV2-L with Grad-CAM offers a reliable and interpretable solution for pneumonia detection, improving diagnostic accuracy and aiding healthcare professionals in clinical decision-making.
Keywords Pneumonia Detection, Chest X-ray, Deep Learning, EfficientNetV2-L, Grad-CAM, CNN, Medical Imaging, Model Interpretability, Image Classification.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-04-04
Cite This A Research of Pneumonia Detection Using EffcientNetV2L with Grad-Cam - Sonadeepthi Meka, Mohana Priya Bonagiri, Venkata Vamsi Krishna Dusanapudi, Yeswanth Ram Kamavarapu, T Uday - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.3245
DOI https://doi.org/10.71097/IJSAT.v16.i2.3245
Short DOI https://doi.org/g9drdr
View / Download PDF File

Share this



CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJSAT DOI prefix is
10.71097/IJSAT

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 15 Isu 4Cover Page Vol 15 Isu 3Cover Page Vol 15 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJSAT
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us Message on WhatsApp +91-9687-182-185 editor@ijsat.org