标题： 利用SAM和迁移学习的早期肺癌检测的高级肺结节分割与分类 显示英文标题

标题： Advanced Lung Nodule Segmentation and Classification for Early Detection of Lung Cancer using SAM and Transfer Learning

摘要： 肺癌是一种极其致命的疾病，主要由于其晚期诊断和高死亡率，使其成为全球癌症相关死亡的主要原因。 基于机器学习（ML）和卷积神经网络（CNN）的深度学习（DL）技术主要用于精确分割和分类CT（计算机断层扫描）或MRI图像中的癌性结节。 本研究介绍了一种通过结合使用Segment Anything Model（SAM）和迁移学习技术来实现肺结节分割的创新方法。 精确分割肺结节对于早期发现肺癌至关重要。 所提出的方法利用边界框提示和视觉变换模型来增强分割性能，实现了高精度、Dice相似性系数（DSC）和交并比（IoU）指标。 SAM与迁移学习的结合显著提高了医学影像中的计算机辅助检测（CAD）系统，特别是在肺癌诊断方面。 研究结果表明，所提出的模型在从CT扫描中精确分割肺结节方面表现出色，突显了其在早期检测和改善肺癌诊断患者护理结果方面的潜力。 结果显示，带有迁移学习的SAM模型在分割任务上的DSC达到了97.08%，IoU达到了95.6%，分类任务的准确率为96.71%，这表明其性能相对于现有技术而言是值得注意的。 显示英文摘要

摘要： Lung cancer is an extremely lethal disease primarily due to its late-stage diagnosis and significant mortality rate, making it the major cause of cancer-related demises globally. Machine Learning (ML) and Convolution Neural network (CNN) based Deep Learning (DL) techniques are primarily used for precise segmentation and classification of cancerous nodules in the CT (Computed Tomography) or MRI images. This study introduces an innovative approach to lung nodule segmentation by utilizing the Segment Anything Model (SAM) combined with transfer learning techniques. Precise segmentation of lung nodules is crucial for the early detection of lung cancer. The proposed method leverages Bounding Box prompts and a vision transformer model to enhance segmentation performance, achieving high accuracy, Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) metrics. The integration of SAM and Transfer Learning significantly improves Computer-Aided Detection (CAD) systems in medical imaging, particularly for lung cancer diagnosis. The findings demonstrate the proposed model effectiveness in precisely segmenting lung nodules from CT scans, underscoring its potential to advance early detection and improve patient care outcomes in lung cancer diagnosis. The results show SAM Model with transfer learning achieving a DSC of 97.08% and an IoU of 95.6%, for segmentation and accuracy of 96.71% for classification indicates that ,its performance is noteworthy compared to existing techniques.