Red Blood Cell Classification Based on Attention Residual Feature Pyramid Network

Abstract

Clinically, red blood cell abnormalities are closely related to tumor diseases, red blood cell diseases, internal medicine, and other diseases. Red blood cell classification is the key to detecting red blood cell abnormalities. Traditional red blood cell classification is done manually by doctors, which requires a lot of manpower produces subjective results. This paper proposes an Attention-based Residual Feature Pyramid Network (ARFPN) to classify 14 types of red blood cells to assist the diagnosis of related diseases. The model performs classification directly on the entire red blood cell image. Meanwhile, a spatial attention mechanism and channel attention mechanism are combined with residual units to improve the expression of category-related features and achieve accurate extraction of features. Besides, the RoI align method is used to reduce the loss of spatial symmetry and improve classification accuracy. Five hundred and eighty eight red blood cell images are used to train and verify the effectiveness of the proposed method. The Channel Attention Residual Feature Pyramid Network (C-ARFPN) model achieves an mAP of 86%; the Channel and Spatial Attention Residual Feature Pyramid Network (CS-ARFPN) model achieves an mAP of 86.9%. The experimental results indicate that our method can classify more red blood cell types and better adapt to the needs of doctors, thus reducing the doctor's time and improving the diagnosis efficiency.

Erysense, a Lab-on-a-Chip-Based Point-of-Care Device to Evaluate Red Blood Cell Flow Properties With Multiple Clinical Applications

“…, of stored RBCs, results in a change in the RBC lateral distribution, especially at high velocities ( Figures 2 , 4 ). Additionally, employing machine learning algorithms enabled us to characterize pathological RBC morphologies under constant flow, similar to previous studies in stasis ( Moon et al, 2021 ; Simionato et al, 2021 ; Song et al, 2021 ; Lamoureux et al, 2022 ). This allowed us to detect morphological changes, distinctive for a certain disease, such as the acanthocytes shown in Figure 2 , or treatment (HDF, Figure 3 ).…”

Computer vision quantitation of erythrocyte shape abnormalities provides diagnostic, prognostic, and mechanistic insight

“…Some previously developed machine-learning methods for the classification of RBC abnormalities have been limited by small or poor-quality data sets, 16 choice of nonstandard classification categories, 33 and limited clinical correlation. 17 , 18 Other approaches have shown good performance in larger or well-defined data sets, 17 , 20 and have often focused on individual cell classification without validation at the smear level or in the context of clinical care. Because human assessment of an individual morphology of a cell will be informed by morphologic heterogeneity across the entire smear, the clinical application of blood smear analysis involves consideration of the overall RBC population.…”

“… 7 , 13 , 14 These systems assist in creating semiquantitative grading but are not sufficiently calibrated for important RBC subtypes, such as schistocytes, 7 , 14 and the corresponding hardware may not be available in resource-limited settings. Research and development of additional tools have been hampered by poor reproducibility, 15 inadequately small image data sets, 16 limited clinical testing, 17 , 18 or narrow focus on a few morphologies. 19 Although some recent approaches have shown promise, 17 , 20 , 21 they have not been validated at the cell population level, at which clinical assessments are made, nor have they been shown to add value in clinical diagnosis.…”

Identification of RNA Markers in Red Blood Cells for Doping Control in Autologous Blood Transfusion

“…Therefore, we concluded that the RNA in the RBCs was stable and unaffected by the storage conditions and consistent with previous reports of human erythrocytes [ 30 , 31 ]. RBCs undergo denucleation during their maturation from blood stem cells before entering the peripheral blood [ 32 , 33 ]. It can also be speculated that the RNA in the RBCs is generated before denucleation.…”