GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images.

IF 2.6 4区 工程技术 Q1 Mathematics
Sonam Aggarwal, Isha Gupta, Ashok Kumar, Sandeep Kautish, Abdulaziz S Almazyad, Ali Wagdy Mohamed, Frank Werner, Mohammad Shokouhifar
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Abstract

Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective tool for analyzing medical images, notably in interpreting endoscopic images, due to their capacity to provide results equivalent to or exceeding those of medical specialists. This capability is particularly crucial in the realm of gastrointestinal disorders, where even experienced gastroenterologists find the automatic diagnosis of such conditions using endoscopic pictures to be a challenging endeavor. Currently, gastrointestinal findings in medical diagnosis are primarily determined by manual inspection by competent gastrointestinal endoscopists. This evaluation procedure is labor-intensive, time-consuming, and frequently results in high variability between laboratories. To address these challenges, we introduced a specialized CNN-based architecture called GastroFuse-Net, designed to recognize human gastrointestinal diseases from endoscopic images. GastroFuse-Net was developed by combining features extracted from two different CNN models with different numbers of layers, integrating shallow and deep representations to capture diverse aspects of the abnormalities. The Kvasir dataset was used to thoroughly test the proposed deep learning model. This dataset contained images that were classified according to structures (cecum, z-line, pylorus), diseases (ulcerative colitis, esophagitis, polyps), or surgical operations (dyed resection margins, dyed lifted polyps). The proposed model was evaluated using various measures, including specificity, recall, precision, F1-score, Mathew's Correlation Coefficient (MCC), and accuracy. The proposed model GastroFuse-Net exhibited exceptional performance, achieving a precision of 0.985, recall of 0.985, specificity of 0.984, F1-score of 0.997, MCC of 0.982, and an accuracy of 98.5%.

GastroFuse-Net:为内窥镜图像中胃肠道异常检测而设计的集合深度学习框架。
卷积神经网络(CNN)作为分析医学图像(尤其是解读内窥镜图像)的高效工具受到了广泛关注,因为它能够提供与医学专家相当或更高的结果。这种能力在胃肠道疾病领域尤为重要,即使是经验丰富的胃肠病学专家也会发现,使用内窥镜图像自动诊断这类疾病是一项极具挑战性的工作。目前,医学诊断中的胃肠道检查结果主要由合格的胃肠道内窥镜医师通过人工检查来确定。这种评估程序耗费大量人力和时间,而且经常导致实验室之间的差异很大。为了应对这些挑战,我们引入了一种基于 CNN 的专门架构,称为 GastroFuse-Net,旨在从内窥镜图像中识别人体胃肠道疾病。GastroFuse-Net 是通过结合从两个不同层数的 CNN 模型中提取的特征而开发的,整合了浅层和深层表征以捕捉异常的不同方面。Kvasir 数据集用于全面测试所提出的深度学习模型。该数据集包含根据结构(盲肠、Z 线、幽门)、疾病(溃疡性结肠炎、食管炎、息肉)或手术操作(染色切除边缘、染色切除息肉)分类的图像。对所提出的模型进行了各种评估,包括特异性、召回率、精确度、F1-分数、马修相关系数(MCC)和准确性。提议的 GastroFuse-Net 模型表现优异,精确度达到 0.985,召回率达到 0.985,特异性达到 0.984,F1 分数达到 0.997,MCC 达到 0.982,准确率达到 98.5%。
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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来源期刊
Mathematical Biosciences and Engineering
Mathematical Biosciences and Engineering 工程技术-数学跨学科应用
CiteScore
3.90
自引率
7.70%
发文量
586
审稿时长
>12 weeks
期刊介绍: Mathematical Biosciences and Engineering (MBE) is an interdisciplinary Open Access journal promoting cutting-edge research, technology transfer and knowledge translation about complex data and information processing. MBE publishes Research articles (long and original research); Communications (short and novel research); Expository papers; Technology Transfer and Knowledge Translation reports (description of new technologies and products); Announcements and Industrial Progress and News (announcements and even advertisement, including major conferences).
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