Automatic Detection and Counting of Blood Cells in Smear Images Using RetinaNet

被引:15
|
作者
Dralus, Grzegorz [1 ]
Mazur, Damian [1 ]
Czmil, Anna [1 ]
机构
[1] Rzeszow Univ Technol, Dept Elect & Comp Engn Fundamentals, PL-35959 Rzeszow, Poland
关键词
confidence threshold; convolution neural networks; platelet; RBC; WBC; SEGMENTATION; COLOR;
D O I
10.3390/e23111522
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
A complete blood count is one of the significant clinical tests that evaluates overall human health and provides relevant information for disease diagnosis. The conventional strategies of blood cell counting include manual counting as well as counting using the hemocytometer and are tedious and time-consuming tasks. This research-based paper proposes an automatic software-based alternative method to count blood cells accurately using the RetinaNet deep learning network, which is used to recognize and classify objects in microscopic images. After training, the network automatically recognizes and counts red blood cells, white blood cells, and platelets. We tested a model trained on smear images and found that the trained model has generalized capabilities. We assessed the quality of detection and cell counting using performance measures, such as accuracy, sensitivity, precision, and F1-score. Moreover, we studied the dependence of the confidence thresholds and the number of learning epochs on the obtained results of recognition and counting. We compared the performance of the proposed approach with those obtained by other authors who dealt with the subject of cell counting and show that object detection and labeling can be an additional advantage in the task of counting objects.
引用
收藏
页数:22
相关论文
共 50 条
  • [1] Automatic identifying and counting blood cells in smear images by using single shot detector and Taguchi method
    Yao-Mei Chen
    Jinn-Tsong Tsai
    Wen-Hsien Ho
    [J]. BMC Bioinformatics, 22
  • [2] Automatic identifying and counting blood cells in smear images by using single shot detector and Taguchi method
    Chen, Yao-Mei
    Tsai, Jinn-Tsong
    Ho, Wen-Hsien
    [J]. BMC BIOINFORMATICS, 2022, 22 (SUPPL 5)
  • [3] Automated counting of white blood cells in thin blood smear images
    Escobar, Francesca Isabelle F.
    Alipo-on, Jacqueline Rose T.
    Novia, Jemima Louise U.
    Tan, Myles Joshua T.
    Karim, Hezerul Abdul
    AlDahoul, Nouar
    [J]. COMPUTERS & ELECTRICAL ENGINEERING, 2023, 108
  • [4] Automatic Counting of Leukocytes in Giemsa-Stained Images of Peripheral Blood Smear
    Hamghalam, Mohammad
    Ayatollahi, Ahmad
    [J]. ICDIP 2009: INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING, PROCEEDINGS, 2009, : 13 - 16
  • [5] Automated Counting of Platelets and White Blood Cells from Blood Smear Images
    Mahanta, Lipi B.
    Bora, Kangkana
    Kalita, Sourav Jyoti
    Yogi, Priyangshu
    [J]. PATTERN RECOGNITION AND MACHINE INTELLIGENCE, PREMI 2019, PT II, 2019, 11942 : 13 - 20
  • [6] Boosting-Based Method for Automatic Detection of Leukocytes in Blood Smear Images
    J. V. Stadelmann
    I. N. Spiridonov
    [J]. Biomedical Engineering, 2012, 46 (4) : 164 - 166
  • [7] White Blood Cell Counting Analysis of Blood Smear Images Using Various Segmentation Strategies
    Safuan, Syadia Nabilah Mohd
    Tomari, Razali
    Zakaria, Wan Nurshazwani Wan
    Othman, Nurmiza
    [J]. ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING: FROM THEORY TO APPLICATIONS, 2017, 1883
  • [8] Automatic melanoma detection and segmentation in dermoscopy images using deep RetinaNet and conditional random fields
    Rehman, Hafeez Ur
    Nida, Nudrat
    Shah, Syed Adnan
    Ahmad, Wakeel
    Faizi, Muhammad Imran
    Anwar, Syed Muhammad
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2022, 81 (18) : 25765 - 25785
  • [9] Automatic melanoma detection and segmentation in dermoscopy images using deep RetinaNet and conditional random fields
    Hafeez ur Rehman
    Nudrat Nida
    Syed Adnan Shah
    Wakeel Ahmad
    Muhammad Imran Faizi
    Syed Muhammad Anwar
    [J]. Multimedia Tools and Applications, 2022, 81 : 25765 - 25785
  • [10] An automated malaria cells detection from thin blood smear images using deep learning
    Sukumarran, D.
    Hasikin, K.
    Khairuddin, Mohd
    Ngui, R.
    Sulaiman, Wan
    Vythilingam, I.
    Divis, P. C. S.
    [J]. TROPICAL BIOMEDICINE, 2023, 40 (02) : 208 - 219