Learning AI-Driven Automated Blood Cell Anomaly Detection: Enhancing Diagnostics and Telehealth in Hematology DOI Creative Commons
Sami Naouali,

Oussama El Othmani

Journal of Imaging, Journal Year: 2025, Volume and Issue: 11(5), P. 157 - 157

Published: May 16, 2025

Hematology plays a critical role in diagnosing and managing wide range of blood-related disorders. The manual interpretation blood smear images, however, is time-consuming highly dependent on expert availability. Moreover, it particularly challenging remote resource-limited settings. In this study, we present an AI-driven system for automated cell anomaly detection, combining computer vision machine learning models to support efficient diagnostics hematology telehealth contexts. Our architecture integrates segmentation (YOLOv11), classification (ResNet50), transfer learning, zero-shot identify categorize types abnormalities from images. Evaluated real annotated samples, the achieved high performance, with precision 0.98, recall 0.99, F1 score 0.98. These results highlight potential proposed enhance diagnostic capabilities clinical decision making underserved regions.

Language: Английский

Security and Privacy Challenges of Large Language Models: A Survey DOI Open Access
Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu

et al.

ACM Computing Surveys, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 13, 2025

Large language models (LLMs) have demonstrated extraordinary capabilities and contributed to multiple fields, such as generating summarizing text, translation, question-answering. Nowadays, LLMs become very popular tools in natural processing (NLP) tasks, with the capability analyze complicated linguistic patterns provide relevant responses depending on context. While offering significant advantages, these are also vulnerable security privacy attacks, jailbreaking data poisoning personally identifiable information (PII) leakage attacks. This survey provides a thorough review of challenges LLMs, along application-based risks various domains, transportation, education, healthcare. We assess extent LLM vulnerabilities, investigate emerging attacks against potential defense mechanisms. Additionally, outlines existing research gaps highlights future directions.

Language: Английский

Citations

17
Explainable Artificial Intelligence for Sustainable Urban Water Systems Engineering DOI Creative Commons

Shofia Saghya Infant,

A.S. Vickram,

A. Saravanan

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104349 - 104349

Published: Feb. 1, 2025

Language: Английский

Citations

2
Quantitative Assessment of Explainability in Machine Learning Models : A Study on the OULA Dataset DOI
Sachini Gunasekara, Mirka Saarela

Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, Journal Year: 2025, Volume and Issue: unknown, P. 101 - 103

Published: March 31, 2025

Language: Английский

Citations

0
Learning AI-Driven Automated Blood Cell Anomaly Detection: Enhancing Diagnostics and Telehealth in Hematology DOI Creative Commons
Sami Naouali,

Oussama El Othmani

Journal of Imaging, Journal Year: 2025, Volume and Issue: 11(5), P. 157 - 157

Published: May 16, 2025

Hematology plays a critical role in diagnosing and managing wide range of blood-related disorders. The manual interpretation blood smear images, however, is time-consuming highly dependent on expert availability. Moreover, it particularly challenging remote resource-limited settings. In this study, we present an AI-driven system for automated cell anomaly detection, combining computer vision machine learning models to support efficient diagnostics hematology telehealth contexts. Our architecture integrates segmentation (YOLOv11), classification (ResNet50), transfer learning, zero-shot identify categorize types abnormalities from images. Evaluated real annotated samples, the achieved high performance, with precision 0.98, recall 0.99, F1 score 0.98. These results highlight potential proposed enhance diagnostic capabilities clinical decision making underserved regions.

Language: Английский

Citations

0