{"title":"基于注意机制的自适应图像分割和多重扩张密度网络的肺气肿智能检测模型","authors":"Indira Linginani, Muddana A. Lakshmi","doi":"10.1016/j.asoc.2024.112483","DOIUrl":null,"url":null,"abstract":"<div><div>Pulmonary emphysema is a significant factor in lung cancer and Chronic Obstructive Pulmonary Disease (COPD). Traditional pulmonary emphysema detection models may have difficulty in accurately detecting and diagnosing the severity of the disease. So, this work developed a novel pulmonary emphysema detection system with the help of deep learning frameworks. Originally, the significant images are accumulated from the benchmark sources, and fed into the Adaptive Trans-DenseUnet (ATDUnet)-based segmentation model. The ATDUnet model is highly effective in accurately segmenting pulmonary emphysema from the gathered images. Moreover, to enhance the segmentation process, the parameters are tuned in the ATDUnet using the Statistical Solution of the Osprey Optimization Algorithm (SSOOA). Subsequently, the segmented image is given to the pulmonary emphysema classification phase, where the Multi-Dilated DenseNet with Attention Mechanism (MDDNet-AM) is employed. By incorporating an attention mechanism, MDDNet-AM can focus on important features within the image for improved accuracy and efficiency in diagnosis. Finally, the developed model offered the pulmonary emphysema classified outcome. Then, the outcome of the developed model is compared against conventional pulmonary emphysema detection methods, and given the accuracy to be 93.10. Therefore, the result proved that the use of developed advanced technology in pulmonary emphysema detection has shown promising results in the field of respiratory health.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"168 ","pages":"Article 112483"},"PeriodicalIF":7.2000,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An intelligent model of pulmonary emphysema detection using adaptive image segmentation and multi-dilated densenet with attention mechanism\",\"authors\":\"Indira Linginani, Muddana A. Lakshmi\",\"doi\":\"10.1016/j.asoc.2024.112483\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Pulmonary emphysema is a significant factor in lung cancer and Chronic Obstructive Pulmonary Disease (COPD). 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By incorporating an attention mechanism, MDDNet-AM can focus on important features within the image for improved accuracy and efficiency in diagnosis. Finally, the developed model offered the pulmonary emphysema classified outcome. Then, the outcome of the developed model is compared against conventional pulmonary emphysema detection methods, and given the accuracy to be 93.10. 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引用次数: 0
摘要
肺气肿是肺癌和慢性阻塞性肺疾病(COPD)的重要因素。传统的肺气肿检测模型可能难以准确检测和诊断疾病的严重程度。因此,本工作在深度学习框架的帮助下开发了一种新的肺气肿检测系统。最初,从基准源中积累重要图像,并将其输入到基于自适应跨密度网(ATDUnet)的分割模型中。ATDUnet模型在从采集的图像中准确分割肺气肿方面非常有效。此外,为了改善分割过程,在ATDUnet中使用鱼鹰优化算法(ssoa)的统计解对参数进行了调整。随后,将分割后的图像输入到肺气肿分类阶段,在此阶段使用Multi-Dilated DenseNet with Attention Mechanism (MDDNet-AM)。通过结合注意机制,MDDNet-AM可以专注于图像中的重要特征,以提高诊断的准确性和效率。最后,建立的模型给出了肺气肿的分类结果。然后,将所建立模型的结果与传统的肺气肿检测方法进行比较,得出准确率为93.10。因此,该结果证明,利用发达的先进技术检测肺气肿在呼吸健康领域显示出良好的效果。
An intelligent model of pulmonary emphysema detection using adaptive image segmentation and multi-dilated densenet with attention mechanism
Pulmonary emphysema is a significant factor in lung cancer and Chronic Obstructive Pulmonary Disease (COPD). Traditional pulmonary emphysema detection models may have difficulty in accurately detecting and diagnosing the severity of the disease. So, this work developed a novel pulmonary emphysema detection system with the help of deep learning frameworks. Originally, the significant images are accumulated from the benchmark sources, and fed into the Adaptive Trans-DenseUnet (ATDUnet)-based segmentation model. The ATDUnet model is highly effective in accurately segmenting pulmonary emphysema from the gathered images. Moreover, to enhance the segmentation process, the parameters are tuned in the ATDUnet using the Statistical Solution of the Osprey Optimization Algorithm (SSOOA). Subsequently, the segmented image is given to the pulmonary emphysema classification phase, where the Multi-Dilated DenseNet with Attention Mechanism (MDDNet-AM) is employed. By incorporating an attention mechanism, MDDNet-AM can focus on important features within the image for improved accuracy and efficiency in diagnosis. Finally, the developed model offered the pulmonary emphysema classified outcome. Then, the outcome of the developed model is compared against conventional pulmonary emphysema detection methods, and given the accuracy to be 93.10. Therefore, the result proved that the use of developed advanced technology in pulmonary emphysema detection has shown promising results in the field of respiratory health.
期刊介绍:
Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities.
Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.