eISSN: 3087-4262 editor@aimjournals.com
All Journals
Submit your article

International Journal of Intelligent Data and Machine Learning

Open Access Peer Review International
Open Access

An Efficient Deep Learning Framework Model for High-Accuracy Image Visual Classification

DOI https://doi.org/10.55640/ijidml-v03i07-04
pdf
Mr. Mohit Sahu 1 ,
4 Assistant Professor, Department of Computer Sciences and Applications, Mandsaur University, Mandsaur, India

Abstract

The term "image" refers to a two-dimensional array of pixels used to depict an item, scene, or pattern in the visual domain. Digital imaging involves the use of imaging equipment, such as cameras, to either capture or fabricate pictures from light or electromagnetic radiation. Training algorithms to identify and classify photos by content is the essence of image classification, a machine learning process. This is accomplished by utilizing the CIFAR-10 dataset, which comprises sixty thousand colour photographs categorized into ten distinct item types, and a framework for high-accuracy picture visual categorization that is based on Recurrent Neural Networks (RNNs). Image normalization, one-hot label encoding, and data augmentation techniques are used to preprocess the dataset in order to prevent overfitting and increase model generalization. In order to acquire useful feature representations and execute reliable image classification, the suggested RNN design has a thick output layer after two recurrent layers. A 97.2% accuracy (ACC) rate, a 97.0% precision (PRE) rate, a 97.6% recall (REC) rate, and an F1-score (F1) of 97.4% were all achieved by the suggested model in the experiments. The suggested RNN achieves a higher classification accuracy (95.49%) than CNN (75.7%), ResNet50 (94.1%), and MobileNetV3 (95.49%), according to a comparison with current deep learning models. These results show that the suggested framework is a high-performance, dependable, and effective way to classify objects in images.

Keywords

Deep learning, Image classification, Convolutional neural networks

References

A. Shakya, M. Biswas, and M. Pal, “Parametric study of convolutional neural network based remote sensing image classification,” Int. J. Remote Sens., vol. 42, no. 7, pp. 2663–2685, Apr. 2021, doi: 10.1080/01431161.2020.1857877.
J. Yang et al., “MedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification,” Sci. Data, vol. 10, no. 1, p. 41, Jan. 2023, doi: 10.1038/s41597-022-01721-8.
M. Naseri et al., “A new cryptography algorithm for quantum images,” Optik (Stuttg)., vol. 171, pp. 947–959, Oct. 2018, doi: 10.1016/j.ijleo.2018.06.113.
J. Maurício, I. Domingues, and J. Bernardino, “Comparing Vision Transformers and Convolutional Neural Networks for Image Classification: A Literature Review,” 2023. doi: 10.3390/app13095521.
M. N. Paralath, “Bridging Astronomical Imagery and Text with Advanced Multi-Modal AI,” 2025 IEEE 7th Int. Conf. Comput. Commun. Autom., pp. 1–8, 2025.
R. Rudra, R. Lingam, S. K. Ravva, and S. A, “A Generalized Deep Learning Approach for Cross-Crop Plant Disease Detection Using the Plant Village Dataset,” J. Mach. Comput., vol. 5, no. 3, pp. 1592–1605, Jul. 2025, doi: 10.53759/7669/jmc202505126.
P. Sharma, Y. P. S. Berwal, and W. Ghai, “Performance analysis of deep learning CNN models for disease detection in plants using image segmentation,” Inf. Process. Agric., 2020, doi: 10.1016/j.inpa.2019.11.001.
M. Naseri, M. Abdolmaleky, F. Parandin, N. Fatahi, A. Farouk, and R. Nazari, “A New Quantum Gray-Scale Image Encoding Scheme*,” Commun. Theor. Phys., vol. 69, no. 2, p. 215, Feb. 2018, doi: 10.1088/0253-6102/69/2/215.
S. Mukherjee, “AI-Driven Deep Learning System for Alzheimer’s Disease (AD) Detection Via MRI Imaging,” in 2026 International Conference on Intelligent Computing, Networks, and Security (IC-ICNS), Bhubaneswar, India: IEEE, 2026, pp. 1–6, March. doi: 10.1109/IC-ICNS68863.2026.11537765.
M. Mittal, “Exploring Generative Adversarial Networks (GANs) For Realistic Image Synthesis,” Int. J. Innov. Res. Comput. Commun. Eng., vol. 06, no. 02, Feb. 2018, doi: 10.15680/IJIRCCE.2018.0602133.
B. Jeganathan, “Exploring the Power of Generative Adversarial Networks (GANs) for Image Generation: A Case Study on the MNIST Dataset,” Int. J. Adv. Eng. Manag., vol. 7, no. 1, pp. 21–46, Jan. 2025, doi: 10.35629/5252-07012146.
R. Nair, S. Vishwakarma, M. Soni, T. Patel, and S. Joshi, “Detection of COVID-19 cases through X-ray images using hybrid deep neural network,” World J. Eng., vol. 19, no. 1, pp. 33–39, Feb. 2022, doi: 10.1108/WJE-10-2020-0529.
S. Singamsetty, “AI-powered Satellite Image Processing for Global Air Traffic Surveillance Techniques Using NCNN–EGSA Optimization Techniques,” in Machine Learning Based Air Traffic Surveillance System Using Image Processing, Emerald Publishing Limited, 2026, pp. 179–197. doi: 10.1108/978-1-80592-062-520251010.
S. A. D. Poovaiah, “Benchmarking provable resilience in convolutional neural networks: A study with Beta-CROWN and ERAN,” Int. J. Sci. Res. Arch., vol. 7, no. 2, pp. 834–845, 2022.
S. Heidari, M. M. Abutalib, M. Alkhambashi, A. Farouk, and M. Naseri, “A new general model for quantum image histogram (QIH),” Quantum Inf. Process., vol. 18, no. 6, p. 175, Jun. 2019, doi: 10.1007/s11128-019-2295-5.
S. Baamonde, M. Cabana, N. Sillero, M. G. Penedo, H. Naveira, and J. Novo, “Fully automatic multi-temporal land cover classification using Sentinel-2 image data,” Procedia Comput. Sci., vol. 159, pp. 650–657, 2019, doi: 10.1016/j.procs.2019.09.220.
M. N. Paralath, “Towards Interpretable and Attentive Deep Visual Perception Through Iterative Refinement in Convolutional Neural Networks,” in 2025 International Conference on Advances in Next-Gen Computer Science (ICANCS), IEEE, Nov. 2025, pp. 1–6. doi: 10.1109/ICANCS65819.2025.11376668.
R. S. S, P. A, K. B, and S. S. Mohamed, “EfficientNet-B0 Based Multi-Class Kidney Abnormality Classification from CT Images Using Transfer Learning,” in 2026 Twelfth International Conference on Bio Signals, Images, and Instrumentation (ICBSII), IEEE, Mar. 2026, pp. 1–6. doi: 10.1109/ICBSII69710.2026.11479194.
V. K, V. S, and K. Gayathri, “Real-Time Defect Classification in Manufacturing using Machine Learning and Image Processing,” in 2026 International Conference on Visual Analytics and Data Visualization (ICVADV), IEEE, Feb. 2026, pp. 1707–1709. doi: 10.1109/ICVADV67766.2026.11470348.
F. Saadati-Monem, A. Mansouri, A. Mahmoudi-Aznaveh, and H. Motamednia, “The Effect of Training Strategy on Image Classification Using Representations of Learning-Based Image Compression,” in 2025 7th International Conference on Pattern Recognition and Image Analysis (IPRIA), IEEE, Sep. 2025, pp. 1–6. doi: 10.1109/IPRIA68579.2025.11263702.
H. Yuan, Z. Xiong, and B. Chen, “Phenotypic Analysis and Variety Classification of Waxy Corn Seeds Based on Machine Vision,” in 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), IEEE, Nov. 2024, pp. 912–915. doi: 10.1109/ICICML63543.2024.10958153.
C. V. Krishna Prasad, V. Amudha, and S. S. H. R, “Accuracy improvement of SAR image classification method using Greedy Hierarchical Learning in comparison with Support Vector Machine,” in 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), IEEE, May 2023, pp. 1–7. doi: 10.1109/ACCAI58221.2023.10200978.
J. Yang, J. Xue, X. Feng, C. Song, and Y. Hao, “Tire Pattern Image Classification using Variational Auto-Encoder with Contrastive Learning,” in 2022 IEEE International Conference on Visual Communications and Image Processing (VCIP), IEEE, Dec. 2022, pp. 1–5. doi: 10.1109/VCIP56404.2022.10008835.
P. Kiran Mehta, R. Jain, and M. Batra, “Image Classification using CNN and CIFAR-10,” Int. Res. J. Eng. Technol., pp. 317–326, 2024.
M. H. Mohiuddin and L. Tamilselvan, “IDedupNet: A MobileNetV3-Based Deep Learning Framework for Efficient Image Deduplication in Cloud Computing Environments,” Informatica, vol. 49, no. 13, Feb. 2025, doi: 10.31449/inf.v49i13.7514.
T. Shahriar, “Comparative Analysis of Lightweight Deep Learning Models for Memory-Constrained Devices,” pp. 1–22, 2025.

Most read articles by the same author(s)

1 2 3 4 5 6 7 > >> 

Similar Articles

51-59 of 59

You may also start an advanced similarity search for this article.