Evaluation of an enhanced ResNet-18 classification model for rapid On-site diagnosis in respiratory cytology

Evaluation of an enhanced ResNet-18 classification model for rapid On-site diagnosis in respiratory cytology

Abstract Objective Rapid on-site evaluation (ROSE) of respiratory cytology specimens is a critical technique for accurate and timely diagnosis of lung cancer. However, in China, limited familiarity with the Diff-Quik staining method and a shortage of trained cytopathologists hamper utilization of RO...

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Main Authors: Wei Gong, Deep K. Vaishnani, Xuan-Chen Jin, Jing Zeng, Wei Chen, Huixia Huang, Yu-Qing Zhou, Khaing Wut Yi Hla, Chen Geng, Jun Ma
Format: Article
Language:English
Published: BMC 2025-01-01
Series:BMC Cancer
Subjects:
Artificial Intelligence (AI)
Lung Cancer
Computer-aided diagnosis
ResNet-18
Rapid on-site evaluation (ROSE)
Online Access:https://doi.org/10.1186/s12885-024-13402-3
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https://doi.org/10.1186/s12885-024-13402-3

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