Core Concepts
Automated report generation for lung cytological images using CNN vision classifier and multiple-transformer text decoders.
Abstract
This study proposes a report-generation technique for lung cytology images using a vision model and text decoders. The method achieved high sensitivity and specificity for classifying benign and malignant cases. The proposed model outperformed existing methods in generating accurate reports. The study outlines the methodology, dataset collection, model evaluation, and performance metrics.
Index:
Abstract
Introduction
Methods
Vision Model
Dataset
Text Decoder
Evaluation Metrics
Results
Discussion
Conclusion
References
Stats
The sensitivity and specificity were 100% and 96.4% for automated benign and malignant case classification.
The classification accuracy for benign and malignant lung cell classification was 79.2%.
Quotes
"The proposed method is useful for pulmonary cytology classification and reporting."