@inproceedings{MTMT:37540068,
	title = {Kidney Stone Detection and Delimitation in Computed Tomography Using YOLOv11},
	url = {https://m2.mtmt.hu/api/publication/37540068},
	author = {Lefkovits, Szidónia and Imre, László and Lefkovits, László},
	booktitle = {Proceedings of the 19th International Conference Inter-Eng 2025 Interdisciplinarity in Engineering Held During 3–4 October 2025},
	doi = {10.1007/978-3-032-17033-0_7},
	unique-id = {37540068},
	year = {2026},
	pages = {96-114}
}

@inproceedings{MTMT:36960996,
	title = {Efficient Deep Learning-Based Fire and Smoke Detection with YOLO Architecture},
	url = {https://m2.mtmt.hu/api/publication/36960996},
	author = {Lefkovits, Szidónia and Lupusor, Nicolae and Lefkovits, László},
	booktitle = {The 18th International Conference Interdisciplinarity in Engineering},
	unique-id = {36960996},
	year = {2025},
	pages = {132-147}
}

@article{MTMT:32799979,
	title = {HGG and LGG Brain Tumor Segmentation in Multi-Modal MRI Using Pretrained Convolutional Neural Networks of Amazon Sagemaker},
	url = {https://m2.mtmt.hu/api/publication/32799979},
	author = {Lefkovits, Szidónia and Lefkovits, László and Szilágyi, László},
	doi = {10.3390/app12073620},
	journal-iso = {APPL SCI-BASEL},
	journal = {APPLIED SCIENCES-BASEL},
	volume = {12},
	unique-id = {32799979},
	year = {2022},
	eissn = {2076-3417},
	orcid-numbers = {Lefkovits, Szidónia/0000-0002-7903-1111; Lefkovits, László/0000-0002-4254-5880}
}

@article{MTMT:33048984,
	title = {Boosting Unsupervised Dorsal Hand Vein Segmentation with U-Net Variants},
	url = {https://m2.mtmt.hu/api/publication/33048984},
	author = {Lefkovits, Szidónia and Emerich, Simina and Lefkovits, László},
	doi = {10.3390/math10152620},
	journal-iso = {MATHEMATICS-BASEL},
	journal = {MATHEMATICS},
	volume = {10},
	unique-id = {33048984},
	abstract = {The identification of vascular network structures is one of the key fields of research in medical imaging. The segmentation of dorsal hand vein patterns form NIR images is not only the basis for reliable biometric identification, but would also provide a significant tool in assisting medical intervention. Precise vein extraction would help medical workers to exactly determine the needle entry point to efficiently gain intravenous access for different clinical purposes, such as intravenous therapy, parenteral nutrition, blood analysis and so on. It would also eliminate repeated attempts at needle pricks and even facilitate an automatic injection procedure in the near future. In this paper, we present a combination of unsupervised and supervised dorsal hand vein segmentation from near-infrared images in the NCUT database. This method is convenient due to the lack of expert annotations of publicly available vein image databases. The novelty of our work is the automatic extraction of the veins in two phases. First, a geometrical approach identifies tubular structures corresponding to veins in the image. This step is considered gross segmentation and provides labels (Label I) for the second CNN-based segmentation phase. We visually observe that different CNNs obtain better segmentation on the test set. This is the reason for building an ensemble segmentor based on majority voting by nine different network architectures (U-Net, U-Net++ and U-Net3+, all trained with BCE, Dice and focal losses). The segmentation result of the ensemble is considered the second label (Label II). In our opinion, the new Label II is a better annotation of the NCUT database than the Label I obtained in the first step. The efficiency of computer vision algorithms based on artificial intelligence algorithms is determined by the quality and quantity of the labeled data used. Furthermore, we prove this statement by training ResNet–UNet in the same manner with the two different label sets. In our experiments, the Dice scores, sensitivity and specificity with ResNet–UNet trained on Label II are superior to the same classifier trained on Label I. The measured Dice scores of ResNet–UNet on the test set increase from 90.65% to 95.11%. It is worth mentioning that this article is one of very few in the domain of dorsal hand vein segmentation; moreover, it presents a general pipeline that may be applied for different medical image segmentation purposes.},
	year = {2022},
	eissn = {2227-7390},
	orcid-numbers = {Lefkovits, Szidónia/0000-0002-7903-1111; Emerich, Simina/0000-0001-9597-4470; Lefkovits, László/0000-0002-4254-5880}
}

@article{MTMT:33357597,
	title = {U-Net architecture variants for brain tumor segmentation of histogram corrected images},
	url = {https://m2.mtmt.hu/api/publication/33357597},
	author = {Lefkovits, Szidónia and Lefkovits, László},
	doi = {10.2478/ausi-2022-0004},
	journal-iso = {ACTA UNIV SAP INFORM},
	journal = {ACTA UNIVERSITATIS SAPIENTIAE INFORMATICA},
	volume = {14},
	unique-id = {33357597},
	issn = {1844-6086},
	abstract = {In this paper we propose to create an end-to-end brain tumor segmentation system that applies three variants of the well-known U-Net convolutional neural networks. In our results we obtain and analyse the detection performances of U-Net, VGG16-UNet and ResNet-UNet on the BraTS2020 training dataset. Further, we inspect the behavior of the ensemble model obtained as the weighted response of the three CNN models. We introduce essential preprocessing and post-processing steps so as to improve the detection performances. The original images were corrected and the different intensity ranges were transformed into the 8-bit grayscale domain to uniformize the tissue intensities, while preserving the original histogram shapes. For post-processing we apply region connectedness onto the whole tumor and conversion of background pixels into necrosis inside the whole tumor. As a result, we present the Dice scores of our system obtained for WT (whole tumor), TC (tumor core) and ET (enhanced tumor) on the BraTS2020 training dataset.},
	keywords = {Brain tumor segmentation; U-Net; histogram uniformization; VGG16-UNet; ResNet5O-UNet; BraTS2020},
	year = {2022},
	eissn = {2066-7760},
	pages = {49-74},
	orcid-numbers = {Lefkovits, Szidónia/0000-0002-7903-1111}
}

@article{MTMT:31936094,
	title = {A review on suppressed fuzzy c-means clustering models},
	url = {https://m2.mtmt.hu/api/publication/31936094},
	author = {Szilágyi, László and Lefkovits, László and Iclanzan, Dávid András},
	doi = {10.2478/ausi-2020-0018},
	journal-iso = {ACTA UNIV SAP INFORM},
	journal = {ACTA UNIVERSITATIS SAPIENTIAE INFORMATICA},
	volume = {12},
	unique-id = {31936094},
	issn = {1844-6086},
	year = {2020},
	eissn = {2066-7760},
	pages = {302-324}
}

@inproceedings{MTMT:31121310,
	title = {Brain Tumor Segmentation and Survival Prediction Using a Cascade of Random Forests},
	url = {https://m2.mtmt.hu/api/publication/31121310},
	author = {Lefkovits, Szidónia and Szilágyi, László and Lefkovits, László},
	booktitle = {Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries : 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018},
	doi = {10.1007/978-3-030-11726-9_30},
	unique-id = {31121310},
	year = {2019},
	pages = {334-345},
	orcid-numbers = {Lefkovits, Szidónia/0000-0002-7903-1111; Szilágyi, László/0000-0001-6722-2642; Lefkovits, László/0000-0002-4254-5880}
}

@inproceedings{MTMT:31121336,
	title = {Applications of Different CNN Architectures for Palm Vein Identification},
	url = {https://m2.mtmt.hu/api/publication/31121336},
	author = {Lefkovits, Szidónia and Lefkovits, László and Szilágyi, László},
	booktitle = {Modeling Decisions for Artificial Intelligence: 16th International Conference, MDAI 2019},
	doi = {10.1007/978-3-030-26773-5_26},
	unique-id = {31121336},
	year = {2019},
	pages = {295-306},
	orcid-numbers = {Lefkovits, Szidónia/0000-0002-7903-1111}
}

@inproceedings{MTMT:33647966,
	title = {Combining Subspace Methods and CNN Segmentation for Iris Identification},
	url = {https://m2.mtmt.hu/api/publication/33647966},
	author = {Lefkovits, Szidónia and Lefkovits, László},
	booktitle = {2019 IEEE 17TH WORLD SYMPOSIUM ON APPLIED MACHINE INTELLIGENCE AND INFORMATICS (SAMI 2019)},
	doi = {10.1109/SAMI.2019.8782780},
	unique-id = {33647966},
	year = {2019},
	pages = {305-310},
	orcid-numbers = {Lefkovits, Szidónia/0000-0002-7903-1111}
}

@article{MTMT:33647976,
	title = {CNN Approaches for Dorsal Hand Vein Based Identification},
	url = {https://m2.mtmt.hu/api/publication/33647976},
	author = {Lefkovits, Szidónia and Lefkovits, László and Szilágyi, László},
	doi = {10.24132/CSRN.2019.2902.2.7},
	journal-iso = {J WSCG},
	journal = {JOURNAL OF WINTER SCHOOL OF COMPUTER GRAPHICS},
	volume = {2019},
	unique-id = {33647976},
	issn = {1213-6972},
	year = {2019},
	eissn = {1213-6964},
	pages = {51-60},
	orcid-numbers = {Lefkovits, Szidónia/0000-0002-7903-1111}
}