Application and Analysis of Some Artificial Intelligence Techniques in Medical Imaging Diagnosis

DOI

10.2991/978-94-6463-823-3_53How to use a DOI?

Keywords

Active learning; Convolutional Neural Networks; Generative Adversarial Networks; Medical Imaging Diagnosis

Abstract

Medical imaging is at the heart of today’s healthcare, and breakthroughs in artificial intelligence have greatly enhanced its diagnostic capabilities. This paper explores the revolutionary contribution of deep learning-primarily Convolutional Neural Networks (CNNs)-to extracting high-level features from complex medical images, thereby improving diagnostic performance in diabetic retinopathy and Alzheimer’s disease. Active learning (AL) significantly reduces the costs associated with annotation by selecting the best informative samples, while generative adversarial networks (GANs) address the problems associated with insufficient data by generating high-quality images for training. Despite their promise, these approaches still suffer from overfitting, insufficient annotated datasets, and ensuring the clinical relevance of the synthesized data. This paper will focus on examples of the application of these three main techniques in medical imaging and further analyze their feasibility for future collaboration. Also by exploring their strengths and limitations, this paper will draw a blueprint for designing better, more efficient and clinically feasible AI diagnostic systems.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Boqian Cao
PY  - 2025
DA  - 2025/08/31
TI  - Application and Analysis of Some Artificial Intelligence Techniques in Medical Imaging Diagnosis
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 532
EP  - 537
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_53
DO  - 10.2991/978-94-6463-823-3_53
ID  - Cao2025
ER  -