Sourour Maalem, Mohammed Mounir Bouhamed, M. Gasmi
{"title":"基于深度的肺癌检测复合模型","authors":"Sourour Maalem, Mohammed Mounir Bouhamed, M. Gasmi","doi":"10.1109/PAIS56586.2022.9946875","DOIUrl":null,"url":null,"abstract":"$X$-ray image analysis is primarily performed by medical specialists. Patients expect a correct interpretation of these images regardless of cost. Despite various advantages of chest radiography, the interpretation of Magnetic Resonance Imaging (MRI) has always been a major issue for the physician and the radiologist due to misdiagnosis. According to the World Health Organization, Lung cancer cost around 1.8 million deaths in 2020, which makes it the leading cause of cancer death worldwide. Late diagnosis and lack of means of screening are the main problems. The algorithm can help radiologists accurately estimate the malignancy risk of lung nodules. This paper aims to detect and classify lung cancer using deep learning. We used the Convolutional Neural Network (CNN) algorithm combined with the Faster Regions with CNN (Fast R-CNN). Our model provides very encouraging results compared to those obtained by the work of the literature, which provides a model with a high accuracy rate for medical assistance.","PeriodicalId":266229,"journal":{"name":"2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A deep-based compound model for lung cancer detection\",\"authors\":\"Sourour Maalem, Mohammed Mounir Bouhamed, M. Gasmi\",\"doi\":\"10.1109/PAIS56586.2022.9946875\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"$X$-ray image analysis is primarily performed by medical specialists. Patients expect a correct interpretation of these images regardless of cost. Despite various advantages of chest radiography, the interpretation of Magnetic Resonance Imaging (MRI) has always been a major issue for the physician and the radiologist due to misdiagnosis. According to the World Health Organization, Lung cancer cost around 1.8 million deaths in 2020, which makes it the leading cause of cancer death worldwide. Late diagnosis and lack of means of screening are the main problems. The algorithm can help radiologists accurately estimate the malignancy risk of lung nodules. This paper aims to detect and classify lung cancer using deep learning. We used the Convolutional Neural Network (CNN) algorithm combined with the Faster Regions with CNN (Fast R-CNN). Our model provides very encouraging results compared to those obtained by the work of the literature, which provides a model with a high accuracy rate for medical assistance.\",\"PeriodicalId\":266229,\"journal\":{\"name\":\"2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS)\",\"volume\":\"58 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PAIS56586.2022.9946875\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PAIS56586.2022.9946875","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
X射线图像分析主要由医学专家进行。不管花费多少,患者都希望对这些图像有一个正确的解释。尽管胸部x线摄影有许多优点,但由于误诊,磁共振成像(MRI)的解释一直是内科医生和放射科医生的主要问题。根据世界卫生组织的数据,2020年肺癌导致约180万人死亡,这使其成为全球癌症死亡的主要原因。诊断晚和缺乏筛查手段是主要问题。该算法可以帮助放射科医生准确估计肺结节的恶性风险。本文旨在利用深度学习对肺癌进行检测和分类。我们使用卷积神经网络(CNN)算法结合Faster - Regions with CNN (Fast R-CNN)。与文献工作的结果相比,我们的模型提供了非常令人鼓舞的结果,为医疗救助提供了一个准确率很高的模型。
A deep-based compound model for lung cancer detection
$X$-ray image analysis is primarily performed by medical specialists. Patients expect a correct interpretation of these images regardless of cost. Despite various advantages of chest radiography, the interpretation of Magnetic Resonance Imaging (MRI) has always been a major issue for the physician and the radiologist due to misdiagnosis. According to the World Health Organization, Lung cancer cost around 1.8 million deaths in 2020, which makes it the leading cause of cancer death worldwide. Late diagnosis and lack of means of screening are the main problems. The algorithm can help radiologists accurately estimate the malignancy risk of lung nodules. This paper aims to detect and classify lung cancer using deep learning. We used the Convolutional Neural Network (CNN) algorithm combined with the Faster Regions with CNN (Fast R-CNN). Our model provides very encouraging results compared to those obtained by the work of the literature, which provides a model with a high accuracy rate for medical assistance.