A Novel Microarray Detection Method Based on Deep Learning with Edge Computing

Traditional microarray scanners typically rely on high-performance computers to process the acquired images. To address these challenges, we propose a novel microarray detection (MD) method designed for deployment on a charge-coupled device (CCD) microarray scanner integrated with an edge computing. In this MD method, we propose a novel model named C2f_SC, which integrates the computational efficiency of the star operation with contextual anchor attention to enhance detection accuracy within a compact model architecture. We further integrate the C2f_SC module into the You Only Look Once v8 (YOLOv8) framework, utilizing MobileNetV3 with the convolutional block attention module (CBAM) as the backbone, removing the detection heads for medium and large objects, replacing the bounding box loss function with the signed intersection over union, and incorporating ResBlock_CBAM before the small object detection head. With the microarray images from the gene expression omnibus database, experimental results demonstrate that our method significantly reduces the required giga floating point operations per seconds (GFLOPs) and model size compared to other YOLO models, while maintaining comparable or higher mAP@50 performance with only a slight drop at mAP@50–95. Specifically, against YOLOv8n, our approach matches mAP@50 while using just 32.9% of GFLOPs and 43.3% of model weights. In addition, we conducted a comparative experiment between the laser confocal scanner and the CCD scanner on the BAC Microarray from MGmed Inc., Republic of Korea (MGMED BAC) to verify the reliability of the CCD scanner. Extensive experimental results demonstrate that the proposed MD method significantly reduces reliance on large-scale computing systems.