{"title":"利用胸部 X 光片检测肺部感染的级联深度学习模型","authors":"Akash Chaturvedi, Shivank Soni","doi":"10.24113/ijoscience.v10i4.498","DOIUrl":null,"url":null,"abstract":"This work focuses on efforts for accurately predicting lung diseases like omicron and pneumonia using chest X-ray imaging, a reliable method in this domain. The work adopts a transfer learning model for lung infection predictions from chest X-ray images. The proposed architecture encompasses both training and testing functions, with key steps including pre-processing, deep feature extraction, and classification. Initially, each X-ray image is enhanced through digital filtering for quality improvement. These processed images are then input into a robust, step-wise learning model that efficiently facilitates the automatic learning of features. The highlight of this approach is the Cascaded learning model, which not only achieves a high accuracy rate of 99% but also significantly reduces computational complexity. This is evidenced by a lower number of training parameters, making the model both more efficient and lightweight, and hence more practical for clinical applications in differentiating between omicron and pneumonia.","PeriodicalId":429424,"journal":{"name":"SMART MOVES JOURNAL IJOSCIENCE","volume":"267 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cascaded Deep Learning Model for Detecting Lung Infections Using Chest X-Rays\",\"authors\":\"Akash Chaturvedi, Shivank Soni\",\"doi\":\"10.24113/ijoscience.v10i4.498\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work focuses on efforts for accurately predicting lung diseases like omicron and pneumonia using chest X-ray imaging, a reliable method in this domain. The work adopts a transfer learning model for lung infection predictions from chest X-ray images. The proposed architecture encompasses both training and testing functions, with key steps including pre-processing, deep feature extraction, and classification. Initially, each X-ray image is enhanced through digital filtering for quality improvement. These processed images are then input into a robust, step-wise learning model that efficiently facilitates the automatic learning of features. The highlight of this approach is the Cascaded learning model, which not only achieves a high accuracy rate of 99% but also significantly reduces computational complexity. This is evidenced by a lower number of training parameters, making the model both more efficient and lightweight, and hence more practical for clinical applications in differentiating between omicron and pneumonia.\",\"PeriodicalId\":429424,\"journal\":{\"name\":\"SMART MOVES JOURNAL IJOSCIENCE\",\"volume\":\"267 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SMART MOVES JOURNAL IJOSCIENCE\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.24113/ijoscience.v10i4.498\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SMART MOVES JOURNAL IJOSCIENCE","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24113/ijoscience.v10i4.498","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
这项工作的重点是利用胸部 X 光成像这一该领域的可靠方法,准确预测肺部疾病(如奥米克隆和肺炎)。该研究采用迁移学习模型从胸部 X 光图像预测肺部感染。提出的架构包括训练和测试功能,关键步骤包括预处理、深度特征提取和分类。首先,通过数字滤波增强每张 X 光图像,以提高质量。然后,将这些经过处理的图像输入一个稳健的分步学习模型,从而有效地促进特征的自动学习。这种方法的亮点是级联学习模型,不仅准确率高达 99%,而且大大降低了计算复杂度。这体现在训练参数的数量更少,使模型更高效、更轻便,因此在临床应用中区分渺小和肺炎更实用。
Cascaded Deep Learning Model for Detecting Lung Infections Using Chest X-Rays
This work focuses on efforts for accurately predicting lung diseases like omicron and pneumonia using chest X-ray imaging, a reliable method in this domain. The work adopts a transfer learning model for lung infection predictions from chest X-ray images. The proposed architecture encompasses both training and testing functions, with key steps including pre-processing, deep feature extraction, and classification. Initially, each X-ray image is enhanced through digital filtering for quality improvement. These processed images are then input into a robust, step-wise learning model that efficiently facilitates the automatic learning of features. The highlight of this approach is the Cascaded learning model, which not only achieves a high accuracy rate of 99% but also significantly reduces computational complexity. This is evidenced by a lower number of training parameters, making the model both more efficient and lightweight, and hence more practical for clinical applications in differentiating between omicron and pneumonia.