An Ensemble of Deep Learning Object Detection Models for Anatomical and Pathological Regions in Brain MRI

Terzı, Ramazan

doi:10.3390/diagnostics13081494

Cited by 4 publications

(1 citation statement)

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“…Gazi Brains 2020 is a new brain MRI dataset generated to meet the needs for qualified MRI datasets. Although a few studies have started to use this dataset [ 26 , 27 , 28 ], it was used for the first time for the in-domain TL. In this study, slices from 50 patients with HGGs were used.…”

Section: Methodsmentioning

confidence: 99%

In-Domain Transfer Learning Strategy for Tumor Detection on Brain MRI

Terzi

Azginoglu

2023

Diagnostics

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Transfer learning has gained importance in areas where there is a labeled data shortage. However, it is still controversial as to what extent natural image datasets as pre-training sources contribute scientifically to success in different fields, such as medical imaging. In this study, the effect of transfer learning for medical object detection was quantitatively compared using natural and medical image datasets. Within the scope of this study, transfer learning strategies based on five different weight initialization methods were discussed. A natural image dataset MS COCO and brain tumor dataset BraTS 2020 were used as the transfer learning source, and Gazi Brains 2020 was used for the target. Mask R-CNN was adopted as a deep learning architecture for its capability to effectively handle both object detection and segmentation tasks. The experimental results show that transfer learning from the medical image dataset was found to be 10% more successful and showed 24% better convergence performance than the MS COCO pre-trained model, although it contains fewer data. While the effect of data augmentation on the natural image pre-trained model was 5%, the same domain pre-trained model was measured as 2%. According to the most widely used object detection metric, transfer learning strategies using MS COCO weights and random weights showed the same object detection performance as data augmentation. The performance of the most effective strategies identified in the Mask R-CNN model was also tested with YOLOv8. Results showed that even if the amount of data is less than the natural dataset, in-domain transfer learning is more efficient than cross-domain transfer learning. Moreover, this study demonstrates the first use of the Gazi Brains 2020 dataset, which was generated to address the lack of labeled and qualified brain MRI data in the medical field for in-domain transfer learning. Thus, knowledge transfer was carried out from the deep neural network, which was trained with brain tumor data and tested on a different brain tumor dataset.

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Section: Methodsmentioning

confidence: 99%

In-Domain Transfer Learning Strategy for Tumor Detection on Brain MRI

Terzi

Azginoglu

2023

Diagnostics

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Synchronizing Object Detection: Applications, Advancements and Existing Challenges

Tanzib Hosain,

Zaman,

Abir

et al. 2024

IEEE Access

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From pivotal roles in autonomous vehicles, healthcare diagnostics, and surveillance systems to seamlessly integrating with augmented reality, object detection algorithms stand as the cornerstone in unraveling the complexities of the visual world. Tracing the trajectory from conventional region-based methods to the latest neural network architectures reveals a technological renaissance where algorithms metamorphose into digital artisans. However, this journey is not without hurdles, prompting researchers to grapple with real-time detection, robustness in varied environments, and interpretability amidst the intricacies of deep learning. The allure of addressing issues such as occlusions, scale variations, and finegrained categorization propels exploration into uncharted territories, beckoning the scholarly community to contribute to an ongoing saga of innovation and discovery. This research offers a comprehensive panorama, encapsulating the applications reshaping our digital reality, the advancements pushing the boundaries of perception, and the open issues extending an invitation to the next generation of visionaries to explore uncharted frontiers within object detection.

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Ensemble Model Object Detection Versus State-of-the-Art Approaches in Performance

Yadav,

Chaware

2024

2024 3rd International Conference for Innovation in Technology (INOCON)

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An Ensemble of Deep Learning Object Detection Models for Anatomical and Pathological Regions in Brain MRI

Cited by 4 publications

References 58 publications

In-Domain Transfer Learning Strategy for Tumor Detection on Brain MRI

In-Domain Transfer Learning Strategy for Tumor Detection on Brain MRI

Synchronizing Object Detection: Applications, Advancements and Existing Challenges

Ensemble Model Object Detection Versus State-of-the-Art Approaches in Performance

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