Browse > Article
http://dx.doi.org/10.33851/JMIS.2021.8.2.79

Comparison of Pre-processed Brain Tumor MR Images Using Deep Learning Detection Algorithms  

Kwon, Hee Jae (Department of Electrical and Computer Engineering, University of Washington)
Lee, Gi Pyo (Department of Biomedical Engineering, Gil Medical Center, Gachon University College of Medicine)
Kim, Young Jae (Department of Biomedical Engineering, Gil Medical Center, Gachon University College of Medicine)
Kim, Kwang Gi (Department of Biomedical Engineering, Gil Medical Center, Gachon University College of Medicine)
Publication Information
Journal of Multimedia Information System / v.8, no.2, 2021 , pp. 79-84 More about this Journal
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.
Keywords
Brain Tumor; RetinaNet; Deep Learning; Histogram Equalization;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Y. Liu, A. Carpenter, H. Yuan, Z. Zhou, M. Zalutsky, G. Vaidyanathan, H. Yan, and T. Vo-Dinh, "Gold nanostar as theranostic probe for brain tumor sensitive PET-optical imaging and image-guided specific photo-thermal therapy," AACR, 2016.
2 M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P. Jodoin, and H. Larochelle, "Brain tumor segmentation with deep neural networks," Medical image analysis, vol. 35, pp. 18-31, 2017.   DOI
3 P. M. Shakeel, T. E. E. Tobely, H. Al-Feel, G. Manogaran, and S. Baskar, "Neural network based brain tumor detection using wireless infrared imaging sensor," IEEE Access, vol. 7 pp. 5577-5588, 2019.   DOI
4 A. M. Reza, "Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement," Journal of VLSI signal processing systems for signal, image and video technology, vol. 38, no. 1, pp. 35-44, 2004.   DOI
5 M. N. Wu, C. C. Lin, and C. C. Chang, "Brain tumor detection using color-based k-means clustering segmentation," in Proceeding of IEEE Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing, vol. 2, pp. 245-250, 2017.
6 M. U. Akram, A. Usman, "Computer aided system for brain tumor detection and segmentation," in Proceeding of IEEE International conference on Computer networks and information technology, pp. 299-302, 2011.
7 Y. T. Kim, "Contrast enhancement using brightness preserving bi-histogram equalization," IEEE transactions on Consumer Electronics, vol. 43 no. 1, pp. 1-8, 1997.   DOI
8 L. Sunwoo, Y. J. Kim, S. H. Choi, K. G. Kim, J. H. Kang, Y. Kang, Y. J. Bae, R. E. Yoo, J. Kim, K. J. Lee, S. H. Lee, B. S. Choi, C. Jung, C. H. Sohn, J. H. Kim, "Computer-aided detection of brain metastasis on 3D MR imaging: Observer performance study," PLoS One, vol. 12, no. 6, 2017.
9 P. Ghosal, L. Nandanwar, S. Kanchan, A. Bhadra, J. Chakraborty, and D. Nandi, "Brain tumor classification using ResNet-101 based squeeze and excitation deep neural network," in Proceeding of IEEE Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), pp. 1-6, Feb. 2019.
10 J. Amin, M. Sharif, M. Yasmin, and S. L. Fernandes, "A distinctive approach in brain tumor detection and classification using MRI," Pattern Recognition Letters. pp. 118-127 2017.
11 A. J. Vyavahare, R. C. Thool, "Segmentation using region growing algorithm based on CLAHE for medical images," in Proceeding of Fourth International Conference on Advances in Recent Technologies in Communication and Computing, pp. 182-185, 2012.
12 He K, Gkioxari G, Dollar P, Girshick R., "Mask r-cnn," in Proceedings of the IEEE international conference on computer vision, pp. 2961-2969, 2017.
13 H. Dong, G. Yang, F. Liu, Y. Mo, and Y. Guo, "Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks," in Proceeding of annual conference on medical image understanding and analysis, Springer, Cham, pp. 506-517, 2017.
14 D. A. Dahab, S. S. Ghoniemy, and G. M. Selim, "Automated brain tumor detection and identification using image processing and probabilistic neural network techniques," International journal of image processing and visual communication, vol. 1, no. 2, pp. 1-8, 2012.
15 R. kumar Rai, P. Gour, B. Singh, "Underwater image segmentation using clahe enhancement and thresholding," International Journal of Emerging Technology and Advanced Engineering, vol. 2, no. 1, pp. 118-123, 2012.
16 S. Sarkar, A. Kumar, S. Chakraborty, S. Aich, J. S. Sim, and H. C. Kim, "A CNN based Approach for the Detection of Brain Tumor Using MRI Scans," Test Engineering and Management, 2020.
17 T. Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, "Focal loss for dense object detection," in Proceedings of the IEEE international conference on computer vision, pp. 2980-2988, 2017.
18 L. Nayak, E. Q. Lee, and P. Y. Wen, "Epidemiology of Brain Metastases," Curr Oncol Reports, vol. 14, no. 1, pp. 48-54, 2012.   DOI