Wireless capsule endoscopes (WCEs) have been used widely to detect abnormalities inside regions of the small intestine that are not accessible when using traditional endoscopy techniques. However, an experienced clini...Wireless capsule endoscopes (WCEs) have been used widely to detect abnormalities inside regions of the small intestine that are not accessible when using traditional endoscopy techniques. However, an experienced clinician must spend an average of 2 hours to view and analyze the approximately 60,000 images produced during one examination. Therefore, developing a computeraided system for processing WCE images is crucial. This paper proposes a novel method used for detecting bleeding and ulcers in WCE images. This approach involves using color features to determine the status of the small intestine. The experimental results revealed that the proposed scheme is promising in detecting bleeding and ulcer regions.展开更多
Due to rapid development in Artificial Intelligence(AI)and Deep Learning(DL),it is difficult to maintain the security and robustness of these techniques and algorithms due to emergence of novel term adversary sampling...Due to rapid development in Artificial Intelligence(AI)and Deep Learning(DL),it is difficult to maintain the security and robustness of these techniques and algorithms due to emergence of novel term adversary sampling.Such technique is sensitive to these models.Thus,fake samples cause AI and DL model to produce diverse results.Adversarial attacks that successfully implemented in real world scenarios highlight their applicability even further.In this regard,minor modifications of input images cause“Adversarial Attacks”that altered the performance of competing attacks dramatically.Recently,such attacks and defensive strategies are gaining lot of attention by the machine learning and security researchers.Doctors use different kinds of technologies to examine the patient abnormalities including Wireless Capsule Endoscopy(WCE).However,using WCE it is very difficult for doctors to detect an abnormality within images since it takes enough time while inspection and deciding abnormality.As a result,it took weeks to generate patients test report,which is tiring and strenuous for them.Therefore,researchers come out with the solution to adopt computerized technologies,which are more suitable for the classification and detection of such abnormalities.As far as the classification is concern,the adversarial attacks generate problems in classified images.Now days,to handle this issue machine learning is mainstream defensive approach against adversarial attacks.Hence,this research exposes the attacks by altering the datasets with noise including salt and pepper and Fast Gradient Sign Method(FGSM)and then reflects that how machine learning algorithms work fine to handle these noises in order to avoid attacks.Results obtained on the WCE images which are vulnerable to adversarial attack are 96.30%accurate and prove that the proposed defensive model is robust when compared to competitive existing methods.展开更多
The distribution of rDNA was visualized in interphase nuclei of tumor promoter treated human lymphocytes in comparison with the mltogen Phytoheamagglutinin (PHA) effects by using an in situ hybridization fluorescent m...The distribution of rDNA was visualized in interphase nuclei of tumor promoter treated human lymphocytes in comparison with the mltogen Phytoheamagglutinin (PHA) effects by using an in situ hybridization fluorescent method. The procedure involves biotinylated rDNA as the probe and FITC-avidin as detection system. Silver (Ag) staining was used to visualize nucleoli. In the interphase nuclei of most of the nonstimulated control lymphocytes, only one small Ag-stained nucleolus could be seen. The in situ hybridization, however, revealed one to several agglomerations of rDNA fluorescent spots. With tumor promoting herb extract WCE (40 μg/ml) or TPA (60 ng/ ml) treatment, the Interphase nucleoli increased slightly in number with the morphology alteration into larger, reticular or compact granular types. Ag-stained particles also increased in number. The number of the in situ hybridization rDNA fluorescent spots and dots increased markedly and largereticulate formations of numerous rDNA spots were seen. This phenomenum resembles to the changes in PHA stimulated lymphocytes. Statistic analysized data showed signifcant difference between control and drug- treated cells. These results indicate that transcriptionally activated rDNA and amplification of total rDNA was induced by the used tumor promoters.展开更多
文摘Wireless capsule endoscopes (WCEs) have been used widely to detect abnormalities inside regions of the small intestine that are not accessible when using traditional endoscopy techniques. However, an experienced clinician must spend an average of 2 hours to view and analyze the approximately 60,000 images produced during one examination. Therefore, developing a computeraided system for processing WCE images is crucial. This paper proposes a novel method used for detecting bleeding and ulcers in WCE images. This approach involves using color features to determine the status of the small intestine. The experimental results revealed that the proposed scheme is promising in detecting bleeding and ulcer regions.
基金This work was supported by“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP),granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea.(No.20204010600090).
文摘Due to rapid development in Artificial Intelligence(AI)and Deep Learning(DL),it is difficult to maintain the security and robustness of these techniques and algorithms due to emergence of novel term adversary sampling.Such technique is sensitive to these models.Thus,fake samples cause AI and DL model to produce diverse results.Adversarial attacks that successfully implemented in real world scenarios highlight their applicability even further.In this regard,minor modifications of input images cause“Adversarial Attacks”that altered the performance of competing attacks dramatically.Recently,such attacks and defensive strategies are gaining lot of attention by the machine learning and security researchers.Doctors use different kinds of technologies to examine the patient abnormalities including Wireless Capsule Endoscopy(WCE).However,using WCE it is very difficult for doctors to detect an abnormality within images since it takes enough time while inspection and deciding abnormality.As a result,it took weeks to generate patients test report,which is tiring and strenuous for them.Therefore,researchers come out with the solution to adopt computerized technologies,which are more suitable for the classification and detection of such abnormalities.As far as the classification is concern,the adversarial attacks generate problems in classified images.Now days,to handle this issue machine learning is mainstream defensive approach against adversarial attacks.Hence,this research exposes the attacks by altering the datasets with noise including salt and pepper and Fast Gradient Sign Method(FGSM)and then reflects that how machine learning algorithms work fine to handle these noises in order to avoid attacks.Results obtained on the WCE images which are vulnerable to adversarial attack are 96.30%accurate and prove that the proposed defensive model is robust when compared to competitive existing methods.
文摘The distribution of rDNA was visualized in interphase nuclei of tumor promoter treated human lymphocytes in comparison with the mltogen Phytoheamagglutinin (PHA) effects by using an in situ hybridization fluorescent method. The procedure involves biotinylated rDNA as the probe and FITC-avidin as detection system. Silver (Ag) staining was used to visualize nucleoli. In the interphase nuclei of most of the nonstimulated control lymphocytes, only one small Ag-stained nucleolus could be seen. The in situ hybridization, however, revealed one to several agglomerations of rDNA fluorescent spots. With tumor promoting herb extract WCE (40 μg/ml) or TPA (60 ng/ ml) treatment, the Interphase nucleoli increased slightly in number with the morphology alteration into larger, reticular or compact granular types. Ag-stained particles also increased in number. The number of the in situ hybridization rDNA fluorescent spots and dots increased markedly and largereticulate formations of numerous rDNA spots were seen. This phenomenum resembles to the changes in PHA stimulated lymphocytes. Statistic analysized data showed signifcant difference between control and drug- treated cells. These results indicate that transcriptionally activated rDNA and amplification of total rDNA was induced by the used tumor promoters.
