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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67023
Volume:
10 |
8 |
Aug 2026
Pages:
1-8
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Science
LNCT Indore
Computer Science
LNCT Indore
The rapid growth of digital images generated through smartphones, surveillance systems, autonomous devices, medical imaging systems, and other digital platforms has created a strong demand for automated image analysis and classification techniques. Traditional machine learning approaches generally depend on manually designed features, which may not effectively represent complex visual patterns. Deep learning, particularly Convolutional Neural Networks (CNNs), provides an effective solution by automatically learning hierarchical features directly from image data. This research presents a CNN-based deep learning framework for image classification using the CIFAR-10 dataset. The proposed framework integrates image preprocessing, normalization, data augmentation, automatic feature extraction, model training, testing, and performance evaluation. The system is implemented using Python and deep learning libraries including TensorFlow and Keras. The model is evaluated using accuracy, precision, recall, F1-score, confusion matrix, ROC curve, and AUC. The reported experimental evaluation achieves 96.40% accuracy, 95.80% precision, 95.30% recall, 95.50% F1-score, and 0.98 AUC. A comparative analysis with traditional machine learning algorithms including Support Vector Machine, K-Nearest Neighbors, Decision Tree, and Random Forest is also presented. The reported results demonstrate the effectiveness of CNN-based deep learning for automated image classification and provide a foundation for developing reliable computer vision applications.
KEYWORDS
This research presented a CNN-based deep learning framework for image classification using the CIFAR-10 dataset. The proposed framework integrates image preprocessing, data augmentation, automatic feature extraction, CNN-based classification, model training, testing, and performance evaluation. The reported experimental evaluation achieved 96.40% accuracy, 95.80% precision, 95.30% recall, 95.50% F1-score, and 0.98 AUC. The comparative analysis also reported higher accuracy for the proposed CNN than SVM, KNN, Decision Tree, and Random Forest. The study demonstrates that deep learning and CNN-based automatic feature extraction can provide an effective approach for image classification. The framework can serve as a foundation for further research in computer vision and intelligent image analysis.
[1] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems, 2012. [2] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv preprint arXiv:1409.1556, 2014. [3] C. Szegedy et al., “Going Deeper with Convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015. [4] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778, 2016. [5] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017. [6] A. G. Howard et al., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” arXiv preprint arXiv:1704.04861, 2017. [7] M. Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” in Proceedings of the 36th International Conference on Machine Learning, vol. 97, pp. 6105–6114, 2019. [8] C. Shorten and T. M. Khoshgoftaar, “A Survey on Image Data Augmentation for Deep Learning,” Journal of Big Data, vol. 6, article 60, 2019. [9] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016. [10] F. Chollet, Deep Learning with Python, 2nd ed. Manning Publications, 2021.
Publication Date
8/31/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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