Comparative exploration of deep convolutional neural networks using real-time endoscopy images

Subhashree Mohapatra , Pukhraj Singh Jeji , Girish Kumar Pati , Manohar Mishra , Tripti Swarnkar
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Abstract

Until now various deep convolutional neural networks are designed and trained for the purpose of classifying different medical conditions related to the domain of gastroenterology. Most of the study carried out have considered publicly available datasets to train the classification networks. Nevertheless, the main motive for carrying out different works in the field gastroenterology is to administer the developed models in healthcare centers in real-world set-ups. For doing so, it is important to check the generalizing ability of the designed systems by regulating them so as to classify endoscopy images captured in a specific hospital. In this regard, the foremost work completed is the collection of the endoscopy data from the hospital and then correctly annotating the images taking the help of a senior endoscopist with experience of more than 5 years. Once the data annotation is completed, the images are segregated into the class of normal and abnormal endoscopy images. Four different models are designed in the current work based on deep learning models, transfer learning models and ensemble approaches, and trained to classify the hospital endoscopy data as normal or abnormal. The models are then tested and evaluated based on various performance measures. It is observed from the comparative analysis that the transfer learning-based ensemble model has the best generalizing ability and gives the best specificity of 100 ​%. It is believed that deep learning-based models can assist endoscopists in add-on to human prediction efficiency.

利用实时内窥镜图像对深度卷积神经网络进行比较探索
迄今为止,人们设计并训练了各种深度卷积神经网络,用于对与肠胃病学领域相关的不同病症进行分类。已开展的大多数研究都考虑使用公开可用的数据集来训练分类网络。然而,在胃肠病学领域开展不同研究的主要动机是在医疗保健中心的实际设置中应用所开发的模型。为此,重要的是通过调节所设计的系统来检查其通用能力,以便对特定医院拍摄的内窥镜图像进行分类。在这方面,首先要完成的工作是从医院收集内窥镜检查数据,然后在一位有 5 年以上经验的资深内窥镜医师的帮助下正确标注图像。数据标注完成后,图像将被分为正常和异常内窥镜图像。目前的工作基于深度学习模型、迁移学习模型和集合方法设计了四种不同的模型,并通过训练将医院内窥镜检查数据分为正常和异常。然后根据各种性能指标对模型进行测试和评估。从比较分析中可以看出,基于迁移学习的集合模型具有最好的泛化能力,特异性最好,达到 100%。相信基于深度学习的模型可以帮助内镜医师提高人类预测效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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