Advancements in Red Blood Cell Detection using Convolutional Neural Networks

2020 ◽  
Author(s):  
František Kajánek ◽  
Ivan Cimrák
Keyword(s):  
Neural Networks ◽  
Blood Cell ◽  
Convolutional Neural Networks ◽  
Red Blood Cell ◽  
Cell Detection

scholarly journals Red blood cell classification in lensless single random phase encoding using convolutional neural networks

2020 ◽  
Vol 28 (22) ◽  
pp. 33504 ◽  
Author(s):  
Timothy O’Connor ◽  
Christopher Hawxhurst ◽  
Leslie M. Shor ◽  
Bahram Javidi
Keyword(s):  
Neural Networks ◽  
Blood Cell ◽  
Convolutional Neural Networks ◽  
Red Blood Cell ◽  
Random Phase ◽  
Cell Classification ◽  
Phase Encoding ◽  
Random Phase Encoding

Automatic Identification of Malaria and Other Red Blood Cell Inclusions Using Convolutional Neural Networks

2021 ◽  
pp. 104680
Author(s):  
Angel Molina ◽  
José Rodellar ◽  
Laura Boldú ◽  
Andrea Acevedo ◽  
Santiago Alférez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Blood Cell ◽  
Convolutional Neural Networks ◽  
Red Blood Cell ◽  
Automatic Identification

Cell detection in pathology and microscopy images with multi-scale fully convolutional neural networks

2018 ◽  
Vol 21 (6) ◽  
pp. 1721-1743 ◽  
Author(s):  
Xipeng Pan ◽  
Dengxian Yang ◽  
Lingqiao Li ◽  
Zhenbing Liu ◽  
Huihua Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Cell Detection ◽  
Multi Scale ◽  
Fully Convolutional Neural Networks ◽  
Microscopy Images

scholarly journals A novel automated image analysis system using deep convolutional neural networks can assist to differentiate MDS and AA

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Konobu Kimura ◽  
Yoko Tabe ◽  
Tomohiko Ai ◽  
Ikki Takehara ◽  
Hiroshi Fukuda ◽  
...  
Keyword(s):  
Neural Networks ◽  
Blood Cell ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Diagnostic System ◽  
Learning System ◽  
Automated Image Analysis ◽  
Image Analysis System ◽  
Cell Image ◽  
Extreme Gradient Boosting

Abstract Detection of dysmorphic cells in peripheral blood (PB) smears is essential in diagnostic screening of hematological diseases. Myelodysplastic syndromes (MDS) are hematopoietic neoplasms characterized by dysplastic and ineffective hematopoiesis, which diagnosis is mainly based on morphological findings of PB and bone marrow. We developed an automated diagnostic support system of MDS by combining an automated blood cell image-recognition system using a deep learning system (DLS) powered by convolutional neural networks (CNNs) with a decision-making system using extreme gradient boosting (XGBoost). The DLS of blood cell image-recognition has been trained using datasets consisting of 695,030 blood cell images taken from 3,261 PB smears including hematopoietic malignancies. The DLS simultaneously classified 17 blood cell types and 97 morphological features of such cells with >93.5% sensitivity and >96.0% specificity. The automated MDS diagnostic system successfully differentiated MDS from aplastic anemia (AA) with high accuracy; 96.2% of sensitivity and 100% of specificity (AUC 0.990). This is the first CNN-based automated initial diagnostic system for MDS using PB smears, which is applicable to develop new automated diagnostic systems for various hematological disorders.

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