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Enhanced Sports Image Classification using Deep CNN Models

by P.R. Krishna Prasad, Harshitha Myneni, S.B.S. Sameer Kumar Metra, Balaji Nelakurthi, Narasimha Naik Meghavath
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 2
Year of Publication: 2025
Authors: P.R. Krishna Prasad, Harshitha Myneni, S.B.S. Sameer Kumar Metra, Balaji Nelakurthi, Narasimha Naik Meghavath
10.5120/ijca2025924791

P.R. Krishna Prasad, Harshitha Myneni, S.B.S. Sameer Kumar Metra, Balaji Nelakurthi, Narasimha Naik Meghavath . Enhanced Sports Image Classification using Deep CNN Models. International Journal of Computer Applications. 187, 2 ( May 2025), 42-49. DOI=10.5120/ijca2025924791

@article{ 10.5120/ijca2025924791,
author = { P.R. Krishna Prasad, Harshitha Myneni, S.B.S. Sameer Kumar Metra, Balaji Nelakurthi, Narasimha Naik Meghavath },
title = { Enhanced Sports Image Classification using Deep CNN Models },
journal = { International Journal of Computer Applications },
issue_date = { May 2025 },
volume = { 187 },
number = { 2 },
month = { May },
year = { 2025 },
issn = { 0975-8887 },
pages = { 42-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number2/enhanced-sports-image-classification-using-deep-cnn-models/ },
doi = { 10.5120/ijca2025924791 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-05-17T02:45:32.665576+05:30
%A P.R. Krishna Prasad
%A Harshitha Myneni
%A S.B.S. Sameer Kumar Metra
%A Balaji Nelakurthi
%A Narasimha Naik Meghavath
%T Enhanced Sports Image Classification using Deep CNN Models
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 2
%P 42-49
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sports image classification is a crucial task in computer vision, facilitating applications such as automated sports analytics and event recognition. This study evaluates the performance of three deep learning models—VGG16, ResNet50, and EfficientNetB0—on a sports image classification dataset.The models were trained and tested using a dataset of sports images, and their performance was assessed based on accuracy, precision, recall, and F1-score. Experimental results indicate that Efficient- NetB0 outperformed the other models, achieving the highest accuracy of 96.6%,precision of 97.35%, recall of 96.6%, and an F1-score of 96.52%. These findings suggest that EfficientNetB0is well-suited for sports image classification, offering a balance of high accuracy and computational efficiency. Its superior performance highlights its potential for real-world applications in sports technology, where fast and accurate classification is essential.

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Index Terms

Computer Science
Information Sciences

Keywords

Sports image classification Deep learning Computer vision Convolutional Neural Networks (CNNs) VGG16 ResNet50 EfficientNetB0.

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