The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Comparing Vision Transformers and Convolutional Neural Networks for Image Classification: A Literature Review
Open accessAbstract
Transformers are models that implement a mechanism of self-attention, individually weighting the importance of each part of the input data. Their use in image classification tasks is still somewhat limited since researchers have so far chosen Convolutional Neural Networks for image classification and transformers were more targeted to Natural Language Processing (NLP) tasks. Therefore, this paper presents a literature review that shows the differences between Vision Transformers (ViT) and Convolutional Neural Networks. The state of the art that used the two architectures for image classification was reviewed and an attempt was made to understand what factors may influence the performance of the two deep learning architectures based on the datasets used, image size, number of target classes (for the classification problems), hardware, and evaluated architectures and top results. The objective of this work is to identify which of the architectures is the best for image classification and under what conditions. This paper also describes the importance of the Multi-Head Attention mechanism for improving the performance of ViT in image classification.
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Cites
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
Jakubův, J., Chvalovský, K., Goertzel, Z., Kaliszyk, C., Olšák, M., Piotrowski, B., Schulz, S., Suda, M., & Urban, J. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,658
Literature review as a research methodology: An overview and guidelines
Snyder, H. (2019) · Journal of Business Research · doi:10.1016/j.jbusres.2019.07.039 · Cited by 11,524
Dataset of breast ultrasound images
Al-Dhabyani, W., Gomaa, M., Khaled, H., & Fahmy, A. (2020) · Data in Brief · doi:10.1016/j.dib.2019.104863 · Cited by 2,405
The extragradient method for finding saddle points and other problems
Korpelevich, G. M. (1976) · Medical Entomology and Zoology · Cited by 1,607
Automated breast ultrasound lesions detection using convolutional neural networks
Yap, M. H., Pons, G., Martí, J., Ganau, S., Sentís, M., Zwiggelaar, R., Davison, A. K., & Martí, R. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2731873 · Cited by 1,086
Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review
Matloob, F., Ghazal, T. M., Taleb, N., Aftab, S., Ahmad, M., Khan, M. A., Abbas, S., & Soomro, T. R. (2021) · IEEE Access · doi:10.1109/access.2021.3095559 · Cited by 293
Vision transformer-based autonomous crack detection on asphalt and concrete surfaces
Asadi Shamsabadi, E., Xu, C., Rao, A. S., Nguyen, T., Ngo, T., & Dias-da-Costa, D. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104316 · Cited by 241
An improved transformer network for skin cancer classification
Xin, C., Liu, Z., Zhao, K., Miao, L., Ma, Y., Zhu, X., Zhou, Q., Wang, S., Li, L., Yang, F., et al. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105939 · Cited by 240
Transformer Neural Network for Weed and Crop Classification of High Resolution UAV Images
Reedha, R., Dericquebourg, E., Canals, R., & Hafiane, A. (2022) · Remote Sensing · doi:10.3390/rs14030592 · Cited by 229
Vision Transformers for Classification of Breast Ultrasound Images
Gheflati, B., & Rivaz, H. (2022) · 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871809 · Cited by 225
Cited by
Pinto-Coelho, L. (2023) · Bioengineering · doi:10.3390/bioengineering10121435 · Cited by 676
A survey of the vision transformers and their CNN-transformer based variants
Khan, A., Rauf, Z., Sohail, A., Khan, A. R., Asif, H., Asif, A., & Farooq, U. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10595-0 · Cited by 451
Park, S.-Y., Ayana, G., Wako, B. D., Jeong, K. C., Yoon, S.-D., & Choe, S.-W. (2025) · Diagnostics · doi:10.3390/diagnostics15030260 · Cited by 18
Metadata
- Authors
- Maurício, J., Domingues, I., & Bernardino, J.
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2023-04-28
- Volume
- 13
- Issue
- 9
- Pages
- 5521
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W4367598041
- Cited by
- 579
- References
- 20
- FWCI
- 52.55
- Type
- journal article
- Language
- en
- Open access
- gold
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
