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

Author:

Terzi Duygu Sinanc1ORCID,Azginoglu Nuh2ORCID

Affiliation:

1. Department of Computer Engineering, Amasya University, Amasya 05100, Turkey

2. Department of Computer Engineering, Kayseri University, Kayseri 38280, Turkey

Abstract

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.

Publisher

MDPI AG

Subject

Clinical Biochemistry

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Utilizing YOLOv5x for the Detection and Classification of Brain Tumors;2024 2nd International Conference on Disruptive Technologies (ICDT);2024-03-15

2. Brain Tumor Detection with Hybrid Segmentation using Improved Adaptive Network Fuzzy Inference System;2024 2nd International Conference on Disruptive Technologies (ICDT);2024-03-15

3. Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study;Heliyon;2024-03

4. Resnet Transfer Learning For Enhanced Medical Image Classification In Healthcare;2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI);2023-12-29

5. Deep-Learning-Based Automated Rotator Cuff Tear Screening in Three Planes of Shoulder MRI;Diagnostics;2023-10-19

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