基于YOLO模型和三维神经网络分类器的CT肺结节分层识别方法。

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2023-11-18 DOI:10.1007/s12194-023-00756-9
Yashar Ahmadyar, Alireza Kamali-Asl, Hossein Arabi, Rezvan Samimi, Habib Zaidi
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引用次数: 0

摘要

本研究旨在帮助医生发现早期肺癌。为了实现这一目标,开发了一种分层系统,可以使用计算机断层扫描(CT)图像检测肺部结节。在初始阶段,使用预先存在的模型(YOLOv5s)检测肺结节。为提高模型的敏感性,建立了0.3的置信度阈值来识别该阶段的结节。分层模型的主要目的是定位和分类所有肺结节,同时尽量减少假阴性率。在对第一阶段结果进行分析之后,开发了一种新的3D卷积神经网络(CNN)分类器,用于对YOLOv5s模型检测到的潜在结节进行检查和分类。目标是创建一个以极低的假阳性率和高准确性为特征的检测框架。使用肺结节分析2016 (LUNA 16)数据集来评估该框架的有效性。该数据集包括888个CT扫描,包括肺中1186个结节和40万个非结节区域的位置。由于低置信度,yolov5技术产生了许多不正确的检测。然而,3D分类系统的加入显著提高了结节识别的精度。通过将使用30%置信限的YOLOv5s方法的结果与3D CNN分类模型相结合,整个系统实现了98.4%的结节检测准确率和98.9%的曲线下面积。尽管会产生一些假阴性和假阳性,但本文提出的从CT扫描中识别肺结节的方法有望作为结节检测决策的有价值的辅助手段。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hierarchical approach for pulmonary-nodule identification from CT images using YOLO model and a 3D neural network classifier.

This study aimed to assist doctors in detecting early-stage lung cancer. To achieve this, a hierarchical system that can detect nodules in the lungs using computed tomography (CT) images was developed. In the initial phase, a preexisting model (YOLOv5s) was used to detect lung nodules. A 0.3 confidence threshold was established for identifying nodules in this phase to enhance the model's sensitivity. The primary objective of the hierarchical model was to locate and categorize all lung nodules while minimizing the false-negative rate. Following the analysis of the results from the first phase, a novel 3D convolutional neural network (CNN) classifier was developed to examine and categorize the potential nodules detected by the YOLOv5s model. The objective was to create a detection framework characterized by an extremely low false positive rate and high accuracy. The Lung Nodule Analysis 2016 (LUNA 16) dataset was used to evaluate the effectiveness of this framework. This dataset comprises 888 CT scans that include the positions of 1186 nodules and 400,000 non-nodular regions in the lungs. The YOLOv5s technique yielded numerous incorrect detections owing to its low confidence level. Nevertheless, the addition of a 3D classification system significantly enhanced the precision of nodule identification. By integrating the outcomes of the YOLOv5s approach using a 30% confidence limit and the 3D CNN classification model, the overall system achieved 98.4% nodule detection accuracy and an area under the curve of 98.9%. Despite producing some false negatives and false positives, the suggested method for identifying lung nodules from CT scans is promising as a valuable aid in decision-making for nodule detection.

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来源期刊
Radiological Physics and Technology
Radiological Physics and Technology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
3.00
自引率
12.50%
发文量
40
期刊介绍: The purpose of the journal Radiological Physics and Technology is to provide a forum for sharing new knowledge related to research and development in radiological science and technology, including medical physics and radiological technology in diagnostic radiology, nuclear medicine, and radiation therapy among many other radiological disciplines, as well as to contribute to progress and improvement in medical practice and patient health care.
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