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1.
001-es BibID:
BIBFORM124274
Első szerző:
Alshuwaili, Dhafer Gheni Honi (Informatics)(PhD)
Cím:
A comparative study of pre-trained models in breast ultrasound image segmentation / Dhafer G. Honi, Mohammed Nsaif, Laszlo Szathmary, Szilvia Szeghalmy
Dátum:
2024
Megjegyzések:
In recent years, several deep learning architectures have emerged achieving impressive results in breast ultrasound image segmentation, despite the fact that the problem itself is challenging, because of the variation in lesion size and unequal distribution of intensity in the lesion area. Many of these methods were trained and evaluated on a specific dataset, the Breast Ultrasound Images (BUSI), as it was one of the first publicly available datasets in the field with expert annotations. However, recently, problems with the dataset have been discovered. We conducted our research to estimate, through a few selected methods, the extent to which problems with the dataset make the performance values reported in recent years unreliable. To achieve this, the selected procedures were trained and evaluated along the same methodology on the original and the cleaned datasets. Our results indicate that results related to the BUSI collection should be treated with serious caution.
ISBN:
979-8-3503-8788-9
Tárgyszavak:
Műszaki tudományok
Informatikai tudományok
konferenciacikk
folyóiratcikk
segmentation
deep learning
breast cancer
ultrasound images
Megjelenés:
2024 IEEE 3rd Conference on Information Technology and Data Science (CITDS) Proceedings /András Hajdu. - (2024), p. 81-86. -
További szerzők:
Nsaif, Mohammed (informatics)
Szathmáry László (1977-) (programtervező-informatikus)
Szeghalmy Szilvia (1984-) (programtervező matematikus)
Internet cím:
Intézményi repozitóriumban (DEA) tárolt változat
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Saját polcon:
2.
001-es BibID:
BIBFORM122479
035-os BibID:
(Scopus)85197031315
Első szerző:
Alshuwaili, Dhafer Gheni Honi (Informatics)(PhD)
Cím:
A one-dimensional convolutional neural network-based deep learning approach for predicting cardiovascular diseases / Dhafer G. Honi, Laszlo Szathmary
Dátum:
2024
ISSN:
2352-9148
Megjegyzések:
Early detection of cardiovascular diseases (CVDs) is crucial for managing cardiovascular diseases and improving patient outcomes. Deep neural networks have the potential to reduce the reliance on costly and time-consuming clinical tests, leading to cost savings for patients and healthcare systems. This study proposes the development of specialized convolutional neural networks for the automated selection of essential variables, employing various preprocessing procedures. It evaluates the approach using the UCI repository heart disease dataset, focusing on early-stage heart disease identification to enhance early prediction and intervention for CVD. To address the challenge of achieving higher accuracy, we introduce an approach using one-dimensional convolutional neural networks, incorporating extensive testing to optimize the network architecture and enhance predictive performance. Additionally, recognizing the impact of features on accuracy, a comprehensive data analysis was performed. Through a meticulous selection process, we identified and utilized key features that significantly influenced the accuracy of our model, contributing to more reliable predictions. Finally, crossvalidation techniques were implemented to precisely evaluate the efficacy of our work. Numerous experiments were conducted to demonstrate the relevance of our research. The prediction accuracy was found to be 99.95% when employing a train-test approach, while it was approximately 98.53% when employing K-Fold crossvalidation. In comparison to existing literature, our approach outperforms a recent best study that proposed a Catboost model, achieving an F1-score of about 92.3% and an average accuracy of 90.94%. This signifies a substantial improvement in predictive performance, with a percentage improvement of approximately 9.90% compared to the Catboost model.
Tárgyszavak:
Műszaki tudományok
Informatikai tudományok
idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Deep learning
Machine learning
Neural networks
Megjelenés:
Informatics in Medicine Unlocked. - 49 (2024), p. 1-14. -
További szerzők:
Szathmáry László (1977-) (programtervező-informatikus)
Internet cím:
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DOI
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