Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)

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Progress in Diagnosis and Prediction of Common Cancers: Multi-Cancer Characteristics, Technical Applications, and AI Model Practices

Authors
Bowen Zhang1, *
1College of Science, Mathematical and Technology, Wenzhou Kean University, Zhejiang, China
*Corresponding author. Email: zhangbow@wku.edu
Corresponding Author
Bowen Zhang
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-648-7_58How to use a DOI?
Keywords
Cancer Diagnosis; Artificial Intelligence; Gastric Cancer; Skin Cancer; Brain tumors
Abstract

Cancer remains a major global health burden, and its early diagnosis and accurate prediction are crucial for improving patient prognosis. This paper reviews three common types of cancers—gastric cancer, skin cancer, and brain tumors—focusing on their pathological mechanisms, pathogenic factors, clinical manifestations, and diagnostic technologies, while emphasizing the application progress of artificial intelligence (AI) models in cancer diagnosis. Research shows that Helicobacter pylori (H. pylori) infection is a core pathogenic factor for gastric cancer, and endoscopy combined with narrow-band imaging technology can significantly improve the accuracy of early diagnosis. Among skin cancers, melanoma has the highest malignancy, and dermoscopy combined with pathological biopsy is the mainstream diagnostic method; AI models in identifying skin cancer images have achieved high accuracy. Gliomas are the most common type of brain tumors, and magnetic resonance imaging combined with gene detection can improve the accuracy of grading; AI models can achieve rapid localization of tumor regions. Current cancer diagnosis still faces challenges such as insufficient sample size, the “black box” issue of AI models, and difficulties in clinical translation. In the future, it is necessary to promote technological implementation by building multi-center databases, developing explainable AI, and strengthening industry-academia-research cooperation.

Copyright
© 2026 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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Volume Title
Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
Series
Advances in Computer Science Research
Publication Date
24 April 2026
ISBN
978-94-6239-648-7
ISSN
2352-538X
DOI
10.2991/978-94-6239-648-7_58How to use a DOI?
Copyright
© 2026 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.

Cite this article

risenwbib
TY  - CONF
AU  - Bowen Zhang
PY  - 2026
DA  - 2026/04/24
TI  - Progress in Diagnosis and Prediction of Common Cancers: Multi-Cancer Characteristics, Technical Applications, and AI Model Practices
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 524
EP  - 532
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_58
DO  - 10.2991/978-94-6239-648-7_58
ID  - Zhang2026
ER  -