Zobrazeno 1 - 10
of 140
pro vyhledávání: '"Andras Hajdu"'
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-8 (2024)
Abstract Machine learning-based systems have become instrumental in augmenting global efforts to combat cervical cancer. A burgeoning area of research focuses on leveraging artificial intelligence to enhance the cervical screening process, primarily
Externí odkaz:
https://doaj.org/article/e61f5a6e66aa45e288c3d2511cb8ba22
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-8 (2024)
Abstract A simple and cheap way to recognize cervical cancer is using light microscopic analysis of Pap smear images. Training artificial intelligence-based systems becomes possible in this domain, e.g., to follow the European recommendation to scree
Externí odkaz:
https://doaj.org/article/6133122870574c5ca0aad42e1f19a742
Publikováno v:
Sensors, Vol 24, Iss 9, p 2926 (2024)
Performing a minimally invasive surgery comes with a significant advantage regarding rehabilitating the patient after the operation. But it also causes difficulties, mainly for the surgeon or expert who performs the surgical intervention, since only
Externí odkaz:
https://doaj.org/article/fcffc2ccd89a44c9aa634600ecb9a0d1
Autor:
Ying Liu, Shujing Zhou, Longbin Wang, Ming Xu, Xufeng Huang, Zhengrui Li, Andras Hajdu, Ling Zhang
Publikováno v:
Frontiers in Microbiology, Vol 14 (2023)
BackgroundIncreasing evidence suggests that people with Coronavirus Disease 2019 (COVID-19) have a much higher prevalence of Acute Myocardial Infarction (AMI) than the general population. However, the underlying mechanism is not yet comprehended. The
Externí odkaz:
https://doaj.org/article/71b83484ed3a48eaa0a338038d405306
Publikováno v:
Mathematics, Vol 11, Iss 23, p 4730 (2023)
Ensemble-based systems are well known to have the capacity to outperform individual approaches if the ensemble members are sufficiently accurate and diverse. This paper investigates how an efficient ensemble of deep convolutional neural networks (CNN
Externí odkaz:
https://doaj.org/article/c208907332c8431395771d2f6e3d2553
Autor:
Ramin Karimi, Andras Hajdu
Publikováno v:
Evolutionary Bioinformatics, Vol 2016, Iss 12, Pp 73-85 (2016)
Externí odkaz:
https://doaj.org/article/173648f44b2d4c7896612c2eaf7b730e
Autor:
Zsolt Torok, Tunde Peto, Eva Csosz, Edit Tukacs, Agnes M. Molnar, Andras Berta, Jozsef Tozser, Andras Hajdu, Valeria Nagy, Balint Domokos, Adrienne Csutak
Publikováno v:
Journal of Diabetes Research, Vol 2015 (2015)
Background. It is estimated that 347 million people suffer from diabetes mellitus (DM), and almost 5 million are blind due to diabetic retinopathy (DR). The progression of DR can be slowed down with early diagnosis and treatment. Therefore our aim wa
Externí odkaz:
https://doaj.org/article/e01c28b1bb9e434689eab4591728b3ab
Autor:
Róbert Lakatos, Attila Tiba, Gergő Bogacsovics, Henrietta Toman, Marcell Beregi-Kovács, Andras Hajdu
Publikováno v:
Annales Mathematicae et Informaticae. 53:73-91
Researchers often use theoretical models which provide a relatively sim- ple, yet concise and effective way of modelling various phenomena. However, it is a well-known fact that the more complex the model, the more complex the mathematical descriptio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b3c112e079c75df6979d6b07c4b8517a
https://doi.org/10.33039/ami.2021.02.003
https://doi.org/10.33039/ami.2021.02.003
Publikováno v:
Annales Mathematicae et Informaticae. 53:219-234
Convolutional Neural Network (CNN) for medical image classification has produced satisfying work [11, 12, 15]. Several pretrained models such as VGG19 [17], InceptionV3 [18], and MobileNet [8] are architectures that can be relied on to design high ac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a558afecf9acc45851c85b8d8b93fcfe
https://doi.org/10.33039/ami.2021.04.001
https://doi.org/10.33039/ami.2021.04.001
Publikováno v:
2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::681573fd8035c4285c5bf836bba41a5f
https://doi.org/10.1109/citds54976.2022.9914092
https://doi.org/10.1109/citds54976.2022.9914092