A CNN-LASSO ensemble classification model for incomplete antibody reactants screening in coombs test

Abstract

BACKGROUND: Precise classification of incomplete antibody reactants (IAR) in the Coombs test is the primary means to prevent incompatible blood transfusions. Currently, an automatic and contactless method is required for accurate IAR classification to avoid human error. OBJECTIVE: We present an ensemble learning algorithm that integrates five convolutional neural networks and the least absolute shrinkage and selection operator (LASSO) regression algorithm into an IAR intensity classification model. METHODS: A dataset including 1628 IAR and corresponding labels of IAR intensity categories ((-), (1+), (2+), (3+), and (4+)) was used. We trained the ensemble model using 1302 IAR and validated its performance using 326 IAR. The optimal ensemble model was used to assist immunologists in classifying IAR. The chord diagrams based on the human-machine interaction were established. RESULTS: The ensemble model achieved 98.8%, 98.4%, 99.7%, 99.5%, and 99.4% accuracies in the (-), (1+), (2+), (3+), and (4+) categories, respectively. The results were compared with those of manual classification by immunologists (average accuracy: 99.2% vs. 75.6%). Using the model, all three immunologists achieved increased accuracy (average accuracy: +8.4%). CONCLUSIONS: The proposed algorithm can thus effectively improve the accuracy and efficiency of IAR intensity classification and facilitate the automation of haemolytic disease screening equipment.