Comparison of Pre-processed Brain Tumor MR Images Using Deep Learning Detection AlgorithmsComparison of Pre-processed Brain Tumor MR Images Using Deep Learning Detection Algorithms
- Other Titles
- Comparison of Pre-processed Brain Tumor MR Images Using Deep Learning Detection Algorithms
- Authors
- Hee Jae Kwon; Gi Pyo Lee; Young Jae Kim; Kwang Gi Kim
- Issue Date
- Jun-2021
- Publisher
- 한국멀티미디어학회
- Keywords
- Brain Tumor; RetinaNet; Deep Learning; Histogram Equalization
- Citation
- Journal of Multimedia Information System, v.8, no.2, pp.79 - 84
- Journal Title
- Journal of Multimedia Information System
- Volume
- 8
- Number
- 2
- Start Page
- 79
- End Page
- 84
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/81411
- ISSN
- 2383-7632
- Abstract
- Detecting brain tumors of different sizes is a challenging task. This study aimed to identify brain tumors using detection algorithms. Most studies in this area use segmentation; however, we utilized detection owing to its advantages. Data were obtained from 64 patients and 11,200 MR images. The deep learning model used was RetinaNet, which is based on ResNet152. The model learned three different types of pre-processing images: normal, general histogram equalization, and contrast-limited adaptive histogram equalization (CLAHE). The three types of images were compared to determine the pre-processing technique that exhibits the best performance in the deep learning algorithms. During pre-processing, we converted the MR images from DICOM to JPG format. Additionally, we regulated the window level and width. The model compared the pre-processed images to determine which images showed adequate performance; CLAHE showed the best performance, with a sensitivity of 81.79%. The RetinaNet model for detecting brain tumors through deep learning algorithms demonstrated satisfactory performance in finding lesions. In future, we plan to develop a new model for improving the detection performance using well-processed data. This study lays the groundwork for future detection technologies that can help doctors find lesions more easily in clinical tasks.
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