利用迁移学习检测胸部 X 光图像中肺炎的分类模型

G. Maquen-Niño, Jhojan Genaro Nuñez-Fernandez, Fany Yesica Taquila-Calderon, Ivan Adrianzén-Olano, Percy De-La-Cruz-VdV, Gilberto Carrión-Barco
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摘要

在当前全球背景下,呼吸系统疾病,尤其是肺炎的发病率大幅上升。这种疾病对五岁以下儿童和 60 岁以上成人的致死率较高,因为如果不及时治疗会导致并发症。本研究利用卷积神经网络(CNN)对图像进行分类,特别是检测是否存在肺炎。本研究采用的数据处理方法是 CRISP-DM。数据集包括从开放存储库 "Kaggle "下载的 5856 张前胸部 X 光片图像,其中 5216 张用于训练,16 张用于验证,624 张用于测试。预处理包括通过修改原始图像、缩放和以张量格式批量分割来增强图像。对转移模型进行了比较分析:DenseNet、VGG19 和 ResNet50 版本 2。每个转移模型都是一个具有四个后续层的 CNN 的头部。这些模型经历了训练、验证和测试阶段。测试结果显示,DenseNet 的准确率为 0.87,VGG19 为 0.86,ResNet50 为 0.91。这些结果肯定了 ResNet50 在图像分类方面的有效性,因为该模型的输出是二进制的,0 表示患者没有肺炎,1 表示患者有肺炎。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification Model Using Transfer Learning for the Detection of Pneumonia in Chest X-Ray Images
In the current global context, there has been a significant increase in respiratory system diseases, particularly pneumonia. This disease has a higher incidence of mortality in children under five years old and adults over 60 years old because it leads to complications if not treated in time. This research leverages convolutional neural networks (CNNs) to classify images, specifically to detect the presence of pneumonia. The data processing methodology utilized in this study is CRISP-DM. The dataset consists of 5,856 images of anteroposterior chest X-rays downloaded from the open repository “Kaggle,” divided into 5,216 images for training, 16 for validation, and 624 for testing. Preprocessing involved image augmentation through modifications to the original images, scaling, and batch division in tensor format. A comparative analysis was conducted among the transfer models: DenseNet, VGG19, and ResNet50 version 2. Each transfer model was the header of a CNN with four subsequent layers. The models underwent training, validation, and testing phases. The test’s results showed that DenseNet achieved an accuracy of 0.87, VGG19 achieved 0.86, and ResNet50 achieved 0.91. These results affirm the effectiveness of ResNet50 in image classification, considering that the model’s output is binary, where 0 represents that the patient does not have pneumonia and 1 indicates that the patient has pneumonia.
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