Combination of Cross Stage Partial Network and GhostNet with Spatial Pyramid Pooling on Yolov4 for Detection of Acute Lymphoblastic Leukemia Subtypes in Multi-Cell Blood Microscopic Image
(1) Department of Informatics, Institut Teknologi Sepuluh Nopember, Indonesia
(2) Department of Informatics, Institut Teknologi Sepuluh Nopember, Indonesia
(3) Department of Informatics, Institut Teknologi Sepuluh Nopember, Indonesia
Abstract
Purpose: Acute Lymphoblastic Leukemia (ALL) Detection with microscopic blood images can use a deep learning-based object detection model to localize and classify ALL cell subtypes. Previous studies only performed single cell-based detection objects or binary classification with leukemia and normal classes. Detection of ALL subtypes is crucial to support early diagnosis and treatment. Therefore, an object detection model is needed to detect ALL subtypes in multi-cell blood microscopic images.
Methods: This study focuses on detecting the ALL subtype using YOLOV4 with a modified neck using Cross Stage Partial Network (CSPNet) and GhostNet. CSPNet is combined with Spatial Pyramid Pooling (SPP) to become SPPCSP to get various features map before the YOLOv4 final layer. Ghostnet was used to reduce the computation time of the modified YOLOV4 neck.
Result: Experimental results show that YOLOv4 SPPCSP outperformed the recall value of 14.6%, the value of [email protected] 0.8%, and reduced the computation time by 4.7 ms compared to the original YOLOv4.
Novelty: The combination of CSPNet and GhostNet for YOLOV4 neck modification can increase the variety of features map and reduce computing time compared to the Original YOLOv4.
Keywords
Full Text:
PDFReferences
L. Boldú, A. Merino, A. Acevedo, A. Molina, and J. Rodellar, “A deep learning model (ALNet) for the diagnosis of acute leukaemia lineage using peripheral blood cell images,” Comput. Methods Programs Biomed., vol. 202, 2021, doi: 10.1016/j.cmpb.2021.105999.
D. Goutam and S. Sailaja, “Classification of acute myelogenous leukemia in blood microscopic images using supervised classifier,” in 2015 IEEE Int. Conf. Eng. Technol., Mar. 2015, pp. 1–5, doi: 10.1109/ICETECH.2015.7275021.
J. Su, J. Han, and J. Song, “A benchmark bone marrow aspirate smear dataset and a multi-scale cell detection model for the diagnosis of hematological disorders,” Comput. Med. Imaging Graph., vol. 90, p. 101912, 2021, doi: https://doi.org/10.1016/j.compmedimag.2021.101912.
J. Su, S. Liu, and J. Song, “A segmentation method based on HMRF for the aided diagnosis of acute myeloid leukemia,” Comput. Methods Programs Biomed., vol. 152, pp. 115–123, Dec. 2017, doi: 10.1016/j.cmpb.2017.09.011.
P. Ghosh, D. Bhattacharjee, M. Nasipuri, and D. K. Basu, “Automatic white blood cell measuring aid for medical diagnosis,” Proc. 2011 Int. Conf. Process Autom. Control Comput. PACC 2011, 2011, doi: 10.1109/PACC.2011.5978895.
N. S. Fatonah, H. Tjandrasa, and C. Fatichah, “Identification of acute lymphoblastic leukemia subtypes in touching cells based on enhanced edge detection,” Int. J. Intell. Eng. Syst., vol. 13, no. 4, pp. 204–215, 2020, doi: 10.22266/IJIES2020.0831.18.
A. Khan, A. Eker, A. Chefranov, and H. Demirel, “White blood cell type identification using multi-layer convolutional features with an extreme-learning machine,” Biomed. Signal Process. Control, vol. 69, p. 102932, 2021, doi: https://doi.org/10.1016/j.bspc.2021.102932.
K. K. Anilkumar, V. J. Manoj, and T. M. Sagi, “Automated Detection of B Cell and T Cell Acute Lymphoblastic Leukaemia Using Deep Learning,” Irbm, vol. 1, pp. 1–9, 2021, doi: 10.1016/j.irbm.2021.05.005.
C. Matek, S. Krappe, C. Münzenmayer, T. Haferlach, and C. Marr, “Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set,” Blood, vol. 138, no. 20, pp. 1917–1927, 2021, doi: 10.1182/blood.2020010568.
A. Benjumea, I. Teeti, F. Cuzzolin, and A. Bradley, “YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles,” 2021, doi: https://doi.org/10.48550/arXiv.2112.11798.
E. K. Wang et al., “Multi-Path Dilated Residual Network for Nuclei Segmentation and Detection,” Cells, vol. 8, no. 5, p. 499, 2019, doi: 10.3390/cells8050499.
R. Khandekar, P. Shastry, S. Jaishankar, O. Faust, and N. Sampathila, “Automated blast cell detection for Acute Lymphoblastic Leukemia diagnosis,” Biomed. Signal Process. Control, vol. 68, no. April, p. 102690, 2021, doi: 10.1016/j.bspc.2021.102690.
B. Yan, P. Fan, X. Lei, Z. Liu, and F. Yang, “A real-time apple targets detection method for picking robot based on improved YOLOv5,” Remote Sens., vol. 13, no. 9, pp. 1–23, 2021, doi: 10.3390/rs13091619.
S. Albahli, N. Nida, A. Irtaza, M. H. Yousaf, and M. T. Mahmood, “Melanoma Lesion Detection and Segmentation Using YOLOv4-DarkNet and Active Contour,” IEEE Access, vol. 8, pp. 198403–198414, 2020, doi: 10.1109/ACCESS.2020.3035345.
E. Prasetyo, N. Suciati, and C. Fatichah, “Yolov4-tiny with wing convolution layer for detecting fish body part,” Comput. Electron. Agric., vol. 198, 2022, doi: 10.1016/j.compag.2022.107023.
B. Richey and M. V. Shirvaikar, “Deep learning based real-time detection of northern corn leaf blight crop disease using YoloV4,” p. 5, 2021, doi: 10.1117/12.2587892.
C. Y. Wang, H. Y. Mark Liao, Y. H. Wu, P. Y. Chen, J. W. Hsieh, and I. H. Yeh, “CSPNet: A new backbone that can enhance learning capability of CNN,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., vol. 2020-June, pp. 1571–1580, 2020, doi: 10.1109/CVPRW50498.2020.00203.
I. Shim, J. H. Lim, Y. W. Jang, J. H. You, S. T. Oh, and Y. K. Kim, “Developing a compressed object detection model based on YOLOv4 for deployment on embedded GPU platform of autonomous system,” Trans. Korean Soc. Automot. Eng., vol. 29, no. 10, pp. 959–966, 2021, doi: 10.7467/KSAE.2021.29.10.959.
K. Han, Y. Wang, Q. Tian, J. Guo, C. Xu, and C. Xu, “GhostNet: More features from cheap operations,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 1577–1586, 2020, doi: 10.1109/CVPR42600.2020.00165.
N. M. Mhaidat et al., “Study the epigenetic down-regulation of Bim on colorectal cancer chemotherapy response,” J. King Saud Univ. - Sci., vol. 31, no. 3, pp. 308–313, 2019, doi: 10.1016/j.jksus.2017.09.012.
A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” arXiv, 2020, doi: https://doi.org/10.48550/arXiv.2004.10934.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 770–778, 2016, doi: 10.1109/CVPR.2016.90.
Z. Yao, Y. Cao, S. Zheng, G. Huang, and S. Lin, “Cross-iteration batch normalization,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 12326–12335, 2021, doi: 10.1109/CVPR46437.2021.01215.
D. Misra, “Mish: A Self Regularized Non-Monotonic Activation Function,” 2019.
W. Zheng, W. Tang, S. Chen, L. Jiang, and C.-W. Fu, “CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud,” 2020, doi: https://doi.org/10.48550/arXiv.2012.03015 .
C. Zhao, Y. Feng, R. Liu, and W. Zheng, “Application of Lightweight Convolution Neural Network in Cancer Diagnosis,” in Proc. 2020 Conf. Artif. Intell. Healthc., 2020, pp. 249–253, doi: 10.1145/3433996.3434042.
H. T. Ismet, T. Mustaqim, and D. Purwitasari, “Aspect Based Sentiment Analysis of Product Review Using Memory Network,” Sci. J. Inform., vol. 9, no. 1, pp. 73–83, 2022, doi: https://doi.org/10.15294/sji.v9i1.34094.
A. Adimas and S. Y. Irianto, “Image Sketch Based Criminal Face Recognition Using Content Based Image Retrieval,” Sci. J. Inform., vol. 8, no. 2, pp. 176–182, 2021, doi: 10.15294/sji.v8i2.27865.
E. Pirdia Wanti, A. Pariyandani, S. Zulkarnain Syed Idrus, and A. Hasudungan Lubis, “Utilization of SVM Method and GLCM Feature Extraction in Classifying Fish Images with Formalin,” Sci. J. Inform., vol. 8, no. 1, 2021, doi: https://doi.org/10.15294/sji.v8i1.26806 .
J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” arXiv, 2018, doi:
https://doi.org/10.48550/arXiv.1804.02767.
Refbacks
- There are currently no refbacks.
Scientific Journal of Informatics (SJI)
p-ISSN 2407-7658 | e-ISSN 2460-0040
Published By Department of Computer Science Universitas Negeri Semarang
Website: https://journal.unnes.ac.id/nju/index.php/sji
Email: [email protected]
This work is licensed under a Creative Commons Attribution 4.0 International License.