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001-es BibID:	BIBFORM122567
035-os BibID:	(Scopus)85196749151
Első szerző:	Bogacsovics Gergő (informatikus)
Cím:	Developing diverse ensemble architectures for automatic brain tumor classification / Gergo Bogacsovics, Balazs Harangi, Andras Hajdu
Dátum:	2024
ISSN:	1380-7501 1573-7721
Megjegyzések:	Brain tumors pose a serious threat in our modern society, with a clear increase in global cases each year. Therefore, developing robust solutions that could automatically and reliably detect brain tumors in their early stages is of utmost importance. In our paper, we revisit the problem of building performant ensembles for clinical usage by maximizing the diversity of the member models during the training procedure. We present an improved, more robust, extended version of our framework and propose solutions that could be integrated into a Computer-Aided Diagnosis system to accurately classify some of the most common types of brain tumors: meningioma, glioma, and pituitary tumors. We show that the new framework based on the histogram loss can be seen as a natural extension of the former approach, as it also calculates the inner products of the latent vectors produced by each member to measure similarity, but at the sametime,it also makesitpossibletocapturemorecomplexpatterns.We also present several variants of our framework to incorporate member models with varying dimensional feature vectors and to cope with imbalanced datasets. We evaluate our solutions on a clinically tested dataset of 3,064 T1-weighted contrast-enhanced magnetic resonance images and show that they greatly outperform other state-of-the-art approaches and the base architectures as well, achieving over 92% accuracy, 92% macro andweightedprecision, 91% macro and 92% weighted F1 score, and over 90% macro and 92% weighted sensitivity.
Tárgyszavak:	idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Deep learning Brain tumor Ensemble learning Diversity Screening systems
Megjelenés:	Multimedia Tools and Applications. - 84 : 30 (2024), p. 36453-36496. -
További szerzők:	Harangi Balázs (1986-) (programtervező matematikus) Hajdu András (1973-) (matematikus, informatikus)
Pályázati támogatás:	ÚNKP-23-3-II-DE-119 Egyéb TKP2021-NKTA-34 Egyéb
Internet cím:	Intézményi repozitóriumban (DEA) tárolt változat DOI
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001-es BibID:	BIBFORM114222
035-os BibID:	(WoS)001060390500001 (Scopus)85169105365
Első szerző:	Sütő József (programtervező informatikus)
Cím:	Improving the generalization capability of YOLOv5 on remote sensed insect trap images with data augmentation / Suto, Jozsef
Dátum:	2024
ISSN:	1380-7501
Megjegyzések:	In agricultural pest management, the traditional insect population tracking in the case of several insect types is based on outsourced sticky paper traps that are checked periodically by a human operator. However, with the aid of the Internet of Things technology and machine learning, this type of manual monitoring can be automated. Even though great progress has been made in the field of insect pest detector models, the lack of sufficient amount of remote sensed trap images prevents their practical application. Beyond the lack of sufficient data, another issue is the large discrepancy between manually taken and remote sensed trap images (different illumination, quality, background, etc.). In order to improve those problems, this paper proposes three previously unused data augmentation approaches (gamma correction, bilateral filtering, and bit-plate slicing) which artificially enrich the training data and through this increase the generalization capability of deep object detectors on remote sensed trap images. Even with the application of the widely used geometric and texture-based augmentation techniques, the proposed methods can further increase the efficiency of object detector models. To demonstrate their efficiency, we used the Faster Regionbased Convolutional Neural Network (R-CNN) and the You Look Only Once version 5 (YOLOv5) object detectors which have been trained on a small set of high-resolution, manually taken trap images while the test set consists of remote sensed images. The experimental results showed that the mean average precision (mAP) of the reference models significantly improved while in some cases their counting error was reduced to a third.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Insect detection YOLOv5 Faster R-CNN Remote sensing Data augmentation
Megjelenés:	Multimedia Tools And Applications. - 83 : 9 (2024), p. 27921-27934. -
Pályázati támogatás:	2020-1.1.2-PIACIKFI-2021-00249 Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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