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

被引:17
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 36 条
[1]   Recognition of peripheral blood cell images using convolutional neural networks [J].
Acevedo, Andrea ;
Alferez, Santiago ;
Merino, Anna ;
Puigvi, Laura ;
Rodellar, Jose .
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2019, 180
[2]   Machine learning approach of automatic identification and counting of blood cells [J].
Alam, Mohammad Mahmudul ;
Islam, Mohammad Tariqul .
HEALTHCARE TECHNOLOGY LETTERS, 2019, 6 (04) :103-108
[3]   Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis [J].
Alzubaidi, Laith ;
Fadhel, Mohammed A. ;
Al-Shamma, Omran ;
Zhang, Jinglan ;
Duan, Ye .
ELECTRONICS, 2020, 9 (03)
[4]  
[Anonymous], 2015, TZUTALIN LABELIMG GI
[5]   A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images [J].
Arslan, Salim ;
Ozyurek, Emel ;
Gunduz-Demir, Cigdem .
CYTOMETRY PART A, 2014, 85A (06) :480-490
[6]   Detection of red and white blood cells from microscopic blood images using a region proposal approach [J].
Di Ruberto, Cecilia ;
Loddo, Andrea ;
Putzu, Lorenzo .
COMPUTERS IN BIOLOGY AND MEDICINE, 2020, 116
[7]  
Dvanesh V. D., 2018, P INT C CURR TRENDS, P1
[8]  
George-Gay Beverly, 2003, J Perianesth Nurs, V18, P96, DOI 10.1053/jpan.2003.50013
[9]  
Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
[10]  
Guijarro-Berdi┬u┬as B., 2019, LECT NOTES COMPUTER, P162