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001-es BibID:BIBFORM101890
035-os BibID:(cikkazonosító)103685 (WoS)000821927500002 (Scopus)85128326328
Első szerző:Bogacsovics Gergő (informatikus)
Cím:Enhancing CNNs through the use of hand-crafted features in automated fundus image classification / Gergo Bogacsovics, János Tóth, András Hajdu, Balázs Harangi
Dátum:2022
ISSN:1746-8094 1746-8108
Megjegyzések:Eye diseases such as diabetic retinopathy and diabetic macular edema pose a major threat in today's world as they affect a significant portion of the global population. Therefore, it is of utmost importance to develop robust solutions that can accurately detect these diseases, especially in their early stages. However, current methods, based on hand-crafted features devised by experts, are not sufficiently accurate. Several solutions have been proposed that use deep learning techniques to improve the performance of such systems. However, they ignore the highly valuable hand-crafted features, that could contribute to more accurate prediction, which underlines the significance of our research. In this paper, we revisit the problem of combining these hand-crafted features with the features extracted by neural networks with the objective of delivering more accurate predictions. We systematically study several state-of-the-art neural networks and methods and propose a number of ways to integrate them into our framework. We show that we arrived at the conclusion that it is possible to achieve significantly better results and outperform networks that do not consider hand-crafted features using the proposed methods.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
deep learning
diabetic macular edema
diabetic retinopathy
ensemble learning
hand-crafted features
screening systems
Megjelenés:Biomedical Signal Processing and Control. - 76 (2022), p. 1-10. -
További szerzők:Tóth János (1984-) (programtervező matematikus) Hajdu András (1973-) (matematikus, informatikus) Harangi Balázs (1986-) (programtervező matematikus)
Pályázati támogatás:ÚNKP-21?3-I-DE-99
Egyéb
ÚNKP-21-5-DE-485
Egyéb
Bolyai János Kutatási Ösztöndíj
MTA
FIKP-20428?3/2018/FEKUTSTRAT
FIKP
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2.

001-es BibID:BIBFORM087061
035-os BibID:(cikkazonosító)102041 (WoS)000575379300006 (Scopus)85087589024
Első szerző:Harangi Balázs (programtervező matematikus)
Cím:Assisted deep learning framework for multi-class skin lesion classification considering a binary classification support / Balazs Harangi, Agnes Baran, Andras Hajdu
Dátum:2020
ISSN:1746-8094 1746-8108
Megjegyzések:In this paper, we propose a deep convolutional neural network framework to classify dermoscopy images into seven classes. With taking the advantage that these classes can be merged into two (healthy/diseased) ones we can train a part of the network regarding this binary task only. Then, the confidences regarding the binary classification are used to tune the multi-class confidence values provided by the other part of the network, since the binary task can be solved more accurately. For both the classification tasks we used GoogLeNet Inception-v3, however, any CNN architectures could be applied for these purposes. The whole network is trained in the usual way, and as our experimental results on the skin lesion image classification show, the accuracy of the multi-class problem has been remarkably raised (by 7% considering the balanced multi-class accuracy) via embedding the more reliable binary classification outcomes.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Assisted learning
Deep learning
Ensemble learning
Skin lesion
Megjelenés:Biomedical Signal Processing and Control. - 62 (2020), p. 1-7. -
További szerzők:Baran Ágnes (1972-) (matematikus) Hajdu András (1973-) (matematikus, informatikus)
Pályázati támogatás:EFOP-3.6.2-16-2017-00015
EFOP
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
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