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The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

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Research Article | Open Access |

Recent Advances in Medical Image Classification

Author 1: Loan Dao Author 2: Ngoc Quoc Ly
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 15, No. 7 · Published 2024 · Cited by 8

DOI: https://doi.org/10.14569/IJACSA.2024.0150727

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Abstract

Medical image classification is crucial for diagnosis and treatment, benefiting significantly from advancements in artificial intelligence. The paper reviews recent progress in the field, focusing on three levels of solutions: basic, specific, and applied. It highlights advances in traditional methods using deep learning models like Convolutional Neural Networks and Vision Transformers, as well as state-of-the-art approaches with Vision-Language Models. These models tackle the issue of limited labeled data, and enhance and explain predictive results through Explainable Artificial Intelligence.

Keywords

Medical Image Classification (MIC) Artificial Intelligence (AI) Vision Transformer (ViT) Vision-Language Model (VLM) eXplainable AI (XAI)

How to Cite this Article

Dao, L., & Ly, N. Q. (2024). Recent Advances in Medical Image Classification. International Journal of Advanced Computer Science and Applications, 15(7). https://doi.org/10.14569/IJACSA.2024.0150727

Dao, Loan, and Ngoc Quoc Ly. "Recent Advances in Medical Image Classification." International Journal of Advanced Computer Science and Applications, vol. 15, no. 7, 2024, https://doi.org/10.14569/IJACSA.2024.0150727.

@article{Dao2024,
  title     = {Recent Advances in Medical Image Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Loan Dao and Ngoc Quoc Ly},
  doi       = {10.14569/IJACSA.2024.0150727},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150727}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

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