A Combined method to identify detection of esophageal cancer by using CNN and LLM

A C TEsophageal cancer is a highly lethal malignancy due to delayed diagnosis and limited treatment options.Early detection is critical for improving patient survival rates and treatment outcomes.This study explores the application of large language models (LLMs) in enhancing the accuracy of esophageal cancer detection.By analyzing patient data, including clinical reports, imaging results, and biomarkers, LLM-based models can identify patterns and anomalies indicative of early-stage esophageal cancer with high precision.Recent studies have demonstrated that LLM-integrated diagnostic models can achieve accuracy rates exceeding 90% in detecting esophageal cancer, outperforming traditional diagnostic methods.The ability of LLMs to process large datasets, recognize complex patterns, and generate predictive insights contributes to improved diagnostic efficiency and reduced false positives and negatives.Furthermore, the integration of LLM-based models with existing clinical workflows offers the potential for non-invasive and cost-effective screening methods.This paper highlights the advantages and challenges of using LLMs in esophageal cancer detection, emphasizing their potential to revolutionize early diagnosis and improve patient outcomes.The findings suggest that LLMbased models represent a promising tool for enhancing accuracy and accessibility in cancer detection, thereby contributing to better patient care and reduced mortality rates.