Blood Typing at the Edge: A Hybrid Deep Learning Pipeline for Point-of-Care Blood Type Classification

Abstract

Blood typing remains a manual, subjective procedure when not reliant on centralized laboratory infrastructure. This study presents an automated blood typing system for point-of-care deployment, developed in collaboration with CRIAM, whose portable device captures reaction images for in vitro diagnostics. The system integrates computer vision and artificial intelligence to classify these reactions automatically. Fourteen classification pipelines were trained and evaluated with a 3090-image dataset, encompassing fine-tuned convolutional neural networks, raw pixel-based classifiers, and hybrid architectures pairing pretrained embeddings from DINOv2 and EfficientNet-B4 with lightweight classifiers. Embedding-based approaches consistently outperformed alternatives in accuracy and cross-fold stability. The best pipeline, in terms of performance and suitability for lowpower devices, combined DINOv2-small embeddings with logistic regression, achieving 99.87 ± 0.12% mean accuracy. After 8-bit integers (INT8) quantization and retraining with data augmentation, accuracy improved to 99.97 ± 0.03%, surpassing the uncompressed baseline. All misclassifications were traced to borderline weak-positive Rh/D reactions, confirming errors are localized and explainable. Held-out validation on 856 images yielded 99.53% accuracy, with the single error attributed to a lighting artifact. While deployment on a legacy 32-bit CPU prototype processes four images in approximately 4.7 min, hardware benchmarking confirmed feasibility, from a Raspberry Pi Zero 2W to high-end mobile processors. These results establish quantized embedding-driven architectures as a solution for automated blood typing in point-of-care and resource-limited settings.Blood typing remains a manual, subjective procedure when not reliant on centralized laboratory infrastructure. This study presents an automated blood typing system for point-of-care deployment, developed in collaboration with CRIAM, whose portable device captures reaction images for in vitro diagnostics. The system integrates computer vision and artificial intelligence to classify these reactions automatically. Fourteen classification pipelines were trained and evaluated with a 3090-image dataset, encompassing fine-tuned convolutional neural networks, raw pixel-based classifiers, and hybrid architectures pairing pretrained embeddings from DINOv2 and EfficientNet-B4 with lightweight classifiers. Embedding-based approaches consistently outperformed alternatives in accuracy and cross-fold stability. The best pipeline, in terms of performance and suitability for lowpower devices, combined DINOv2-small embeddings with logistic regression, achieving 99.87 ± 0.12% mean accuracy. After 8-bit integers (INT8) quantization and retraining with data augmentation, accuracy improved to 99.97 ± 0.03%, surpassing the uncompressed baseline. All misclassifications were traced to borderline weak-positive Rh/D reactions, confirming errors are localized and explainable. Held-out validation on 856 images yielded 99.53% accuracy, with the single error attributed to a lighting artifact. While deployment on a legacy 32-bit CPU prototype processes four images in approximately 4.7 min, hardware benchmarking confirmed feasibility, from a Raspberry Pi Zero 2W to high-end mobile processors. These results establish quantized embedding-driven architectures as a solution for automated blood typing in point-of-care and resource-limited settings.Blood typing remains a manual, subjective procedure when not reliant on centralized laboratory infrastructure. This study presents an automated blood typing system for point-of-care deployment, developed in collaboration with CRIAM, whose portable device captures reaction images for in vitro diagnostics. The system integrates computer vision and artificial intelligence to classify these reactions automatically. Fourteen classification pipelines were trained and evaluated with a 3090-image dataset, encompassing fine-tuned convolutional neural networks, raw pixel-based classifiers, and hybrid architectures pairing pretrained embeddings from DINOv2 and EfficientNet-B4 with lightweight classifiers. Embedding-based approaches consistently outperformed alternatives in accuracy and cross-fold stability. The best pipeline, in terms of performance and suitability for lowpower devices, combined DINOv2-small embeddings with logistic regression, achieving 99.87 ± 0.12% mean accuracy. After 8-bit integers (INT8) quantization and retraining with data augmentation, accuracy improved to 99.97 ± 0.03%, surpassing the uncompressed baseline. All misclassifications were traced to borderline weak-positive Rh/D reactions, confirming errors are localized and explainable. Held-out validation on 856 images yielded 99.53% accuracy, with the single error attributed to a lighting artifact. While deployment on a legacy 32-bit CPU prototype processes four images in approximately 4.7 min, hardware benchmarking confirmed feasibility, from a Raspberry Pi Zero 2W to high-end mobile processors. These results establish quantized embedding-driven architectures as a solution for automated blood typing in point-of-care and resource-limited settings.