Accurate diagnosis of non‐small cell and small cell lung cancer by using machine learning models trained with physical science features extracted from pathological images | Synapse
Accurate diagnosis of non‐small cell and small cell lung cancer by using machine learning models trained with physical science features extracted from pathological images
The research aims to evaluate machine learning models for distinguishing between non-small cell lung cancer and small cell lung cancer using physical science features.
Developed machine learning models using physical science features from pathological images.
Trained and tested models to assess accuracy in differentiating lung cancer types.
Machine learning models demonstrated high accuracy in identifying non-small cell lung cancer and small cell lung cancer.
The approach shows potential to enhance diagnostic precision compared to traditional methods.
Abstract
Machine learning models trained on physical science features have the potential to serve as a highly accurate and robust framework for differentiating NSCLC from SCLC.