Correction: Segmentation and Classification of Bone Marrow Cells Images Using Contextual Information for Medical Diagnosis of Acute Leukemias

Improved YOLOv7 Algorithm for Detecting Bone Marrow Cells

The detection and classification of bone marrow (BM) cells is a critical cornerstone for hematology diagnosis. However, the low accuracy caused by few BM-cell data samples, subtle difference between classes, and small target size, pathologists still need to perform thousands of manual identifications daily. To address the above issues, we propose an improved BM-cell-detection algorithm in this paper, called YOLOv7-CTA. Firstly, to enhance the model's sensitivity to fine-grained features, we design a new module called CoTLAN in the backbone network to enable the model to perform long-term modeling between target feature information. Then, in order to cooperate with the CoTLAN module to pay more attention to the features in the area to be detected, we integrate the coordinate attention (CoordAtt) module between the CoTLAN modules to improve the model's attention to small target features. Finally, we cluster the target boxes of the BM cell dataset based on K-means++ to generate more suitable anchor boxes, which accelerates the convergence of the improved model. In addition, in order to solve the imbalance between positive and negative samples in BM-cell pictures, we use the Focal loss function to replace the multi-class cross entropy. Experimental results demonstrate that the best mean average precision (mAP) of the proposed model reaches 88.6%, which is an improvement of 12.9%, 8.3%, and 6.7% compared with that of the Faster R-CNN model, YOLOv5l model, and YOLOv7 model, respectively. This verifies the effectiveness and superiority of the YOLOv7-CTA model in BM-cell-detection tasks.

Cell Population Data-Driven Acute Promyelocytic Leukemia Flagging Through Artificial Neural Network Predictive Modeling

A targeted and timely offered treatment can be a benefitting tool for patients with acute promyelocytic leukemia (APML). Current round of study made use of potential morphological and immature fraction-related parameters (cell population data) generated during complete blood cell count (CBC), through artificial neural network (ANN) predictive modeling for early flagging of APML cases. We collected classical CBC items along with cell population data (CPD) from hematology analyzer at diagnosis of 1067 study subjects with hematological neoplasms. For morphological assessment, peripheral blood films were examined. Statistical and machine learning tools including principal component analysis (PCA) helped in the evaluation of predictive capacity of routine and CPD items. Then selected CBC item-driven ANN predictive modeling was developed to smartly use the hidden trend by increasing the auguring accuracy of these parameters in differentiation of APML cases. We found a characteristic triad based on lower (53.73) platelet count (PLT) with decreased/normal (4.72) immature fraction of platelet (IPF) with addition of significantly higher value (65.5) of DNA/RNA content-related neutrophil (NE-SFL) parameter in patients with APML against other hematological neoplasm's groups. On PCA, APML showed exceptionally significant variance for PLT, IPF, and NE-SFL. Through training of ANN predictive modeling, our selected CBC items successfully classify the APML group from non-APML groups at highly significant (0.894) AUC value with lower (2.3 percent) false prediction rate. Practical results of using our ANN model were found acceptable with value of 95.7% and 97.7% for training and testing data sets, respectively. We proposed that PLT, IPF, and NE-SFL could potentially be used for early flagging of APML cases in the hematology-oncology unit. CBC item-driven ANN modeling is a novel approach that substantially strengthen the predictive potential of CBC items, allowing the clinicians to be confident by the typical trend raised by these studied parameters.