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Deep Learning Approaches for Image Classification

Published: 15 March 2023 Publication History

Abstract

Deep learning models can achieve a higher accuracy result compared with traditional machine learning algorithm. It is widely useful in different areas, especially in images classification area. In recent years, because of the improvement of hardware and the discovery of new deep learning network structures, the accuracy and reliability of deep learning model used in image classification have been greatly improved. However, in the field of images classification with deep learning technology, the reviews of the recent researches are lack. This paper will make a review about the recent researches of images classification based on deep learning. It includes the latest studies to improve the performance about deep learning. Additionally, the potential problems and challenges on deep learning technology and the possible future improvement and research direction are analyzed and discussed in the review.

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Cited By

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  • (2024)Deep learning for medicinal plant species classification and recognition: a systematic reviewFrontiers in Plant Science10.3389/fpls.2023.128608814Online publication date: 5-Jan-2024
  • (2024)Enhancing the Improving Recall of Image Recognition for Deep Learning Algorithm Using Densnet-169 Compared with VGG192024 2nd World Conference on Communication & Computing (WCONF)10.1109/WCONF61366.2024.10692287(1-6)Online publication date: 12-Jul-2024
  • (2024)Single-Step Support Set Mining for Realistic Few-Shot Image Classification2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651328(1-8)Online publication date: 30-Jun-2024

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EITCE '22: Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering
October 2022
1999 pages
ISBN:9781450397148
DOI:10.1145/3573428
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 March 2023

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Author Tags

  1. Convolution neural network
  2. Deep learning
  3. Image classification

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EITCE 2022

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Overall Acceptance Rate 508 of 972 submissions, 52%

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Cited By

View all
  • (2024)Deep learning for medicinal plant species classification and recognition: a systematic reviewFrontiers in Plant Science10.3389/fpls.2023.128608814Online publication date: 5-Jan-2024
  • (2024)Enhancing the Improving Recall of Image Recognition for Deep Learning Algorithm Using Densnet-169 Compared with VGG192024 2nd World Conference on Communication & Computing (WCONF)10.1109/WCONF61366.2024.10692287(1-6)Online publication date: 12-Jul-2024
  • (2024)Single-Step Support Set Mining for Realistic Few-Shot Image Classification2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651328(1-8)Online publication date: 30-Jun-2024

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