Deep neural network approaches for detecting gastric polyps in endoscopic images,Medical & Biological Engineering & Computing

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Deep neural network approaches for detecting gastric polyps in endoscopic images
Medical & Biological Engineering & Computing ( IF 2.6 ) Pub Date : 2021-07-14 , DOI: 10.1007/s11517-021-02398-8
Serdar Durak ₁ , Bülent Bayram ₂ , Tolga Bakırman ₂ , Murat Erkut ₁ , Metehan Doğan ₂ , Mert Gürtürk ₂ , Burak Akpınar ₂

Affiliation

Gastrointestinal endoscopy is the primary method used for the diagnosis and treatment of gastric polyps. The early detection and removal of polyps is vitally important in preventing cancer development. Many studies indicate that a high workload can contribute to misdiagnosing gastric polyps, even for experienced physicians. In this study, we aimed to establish a deep learning–based computer-aided diagnosis system for automatic gastric polyp detection. A private gastric polyp dataset was generated for this purpose consisting of 2195 endoscopic images and 3031 polyp labels. Retrospective gastrointestinal endoscopy data from the Karadeniz Technical University, Farabi Hospital, were used in the study. YOLOv4, CenterNet, EfficientNet, Cross Stage ResNext50-SPP, YOLOv3, YOLOv3-SPP, Single Shot Detection, and Faster Regional CNN deep learning models were implemented and assessed to determine the most efficient model for precancerous gastric polyp detection. The dataset was split 70% and 30% for training and testing all the implemented models. YOLOv4 was determined to be the most accurate model, with an 87.95% mean average precision. We also evaluated all the deep learning models using a public gastric polyp dataset as the test data. The results show that YOLOv4 has significant potential applicability in detecting gastric polyps and can be used effectively in gastrointestinal CAD systems.

Graphical abstract

Gastric Polyp Detection Process using Deep Learning with Private Dataset

中文翻译：

用于检测内窥镜图像中胃息肉的深度神经网络方法

胃肠内镜检查是诊断和治疗胃息肉的主要方法。息肉的早期发现和切除对于预防癌症发展至关重要。许多研究表明，即使是有经验的医生，高工作量也会导致误诊胃息肉。在这项研究中，我们旨在建立一个基于深度学习的计算机辅助诊断系统，用于自动检测胃息肉。为此目的生成了一个私人胃息肉数据集，包括 2195 个内窥镜图像和 3031 个息肉标签。该研究使用了来自法拉比医院 Karadeniz 技术大学的回顾性胃肠内窥镜检查数据。YOLOv4、CenterNet、EfficientNet、跨阶段 ResNext50-SPP、YOLOv3、YOLOv3-SPP、单次检测、和更快的区域 CNN 深度学习模型被实施和评估，以确定最有效的癌前胃息肉检测模型。数据集分为 70% 和 30%，用于训练和测试所有实现的模型。YOLOv4 被确定为最准确的模型，平均精度为 87.95%。我们还使用公共胃息肉数据集作为测试数据评估了所有深度学习模型。结果表明，YOLOv4在检测胃息肉方面具有显着的潜在适用性，可有效应用于胃肠CAD系统。95% 平均精度。我们还使用公共胃息肉数据集作为测试数据评估了所有深度学习模型。结果表明，YOLOv4在检测胃息肉方面具有显着的潜在适用性，可有效应用于胃肠CAD系统。95% 平均精度。我们还使用公共胃息肉数据集作为测试数据评估了所有深度学习模型。结果表明，YOLOv4在检测胃息肉方面具有显着的潜在适用性，可有效应用于胃肠CAD系统。

图形概要

使用私人数据集深度学习的胃息肉检测过程

更新日期：2021-07-14

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