## 1 Introduction
The OrgaNet algorithm developed by [PERSON] et al. [17] achieved a 96.9% classification accuracy for organoid viability on a dedicated dataset but demonstrated limited adaptability to bright-field imaging. To overcome this limitation, [PERSON] et al. [18] introduced DeepOrganoid, a model integrating bright-field microscopy, Z-stack projections, and biochemical multimodal data. Through cross-dimensional feature fusion, it achieved 97% [EMAIL_ADDRESS] for viability assessment and classification, supporting dynamic, personalized treatment decision-making. [PERSON] et al. [19] proposed a synergistic framework combining U-Net for microscopic image feature extraction and DenseNet-201 for the classification of six colorectal cancer organoid subtypes based on textural characteristics. This approach enabled both morphological and functional analysis, achieving an [EMAIL_ADDRESS] of 73.6%. [PERSON] et al. [20] developed D-CryptO, a deep learning tool that attained [EMAIL_ADDRESS] scores of 98% and 90.87% on two colorectal organoid datasets, while also facilitating quantitative assessment of structural maturity. [PERSON] et al. [21] introduced OrgaTracker, which integrates YOLOv5 with U-Net and was trained on datasets of human colon tumors and murine intestinal organoids. It achieved an [EMAIL_ADDRESS] of 96.4% by combining real-time regional segmentation with dynamic fusion analysis, thus enabling multidimensional evaluation of organoid quality. Addressing morphological diversity in intestinal organoids, [PERSON] et al. [22] developed Tellu, a YOLOv5-based model capable of category-sensitive analysis and real-time processing, achieving an [EMAIL_ADDRESS] of 79%. [PERSON] et al. [23] constructed a gastric organoid senescence detection model by embedding the convolutional block attention module (CBAM) into the YOLOv3 architecture. This enabled fine-grained recognition of senescence features under complex microscopic conditions, achieving an [EMAIL_ADDRESS] of 93.2%. [PERSON] et al. [24] developed Deep-Orga based on a YOLOv architecture, attaining [EMAIL_ADDRESS] scores of 90% and 81% on human and murine intestinal datasets, respectively, thereby demonstrating robust cross-sample generalization. To address classification challenges between cystic and solid colorectal cancer organoids, [PERSON] et al. [25] evaluated six object detection models. YOLOv4 achieved the highest [EMAIL_ADDRESS] of 36.9%, revealing a significant correlation between these morphological subtypes and biologically viable organoids. In contrast, [PERSON] et al. [26] proposed Deliod, an optimized YOLOv88-based architecture. It achieved an [EMAIL_ADDRESS] of 87.5% on murine intestinal organoid detection tasks, while significantly reducing computational and parameter overhead, making it suitable for edge-device deployment. In summary, while object detection models have advanced organoid morphological analysis, two persistent challenges hinder high-throughput application: (1) the need for fast and accurate target localization and classification and (2) model lightweighting to enable edge-device deployment. Existing deep learning models often struggle to strike a balance between computational efficiency and performance, limiting their utility in real-world high-throughput settings. To address these challenges, this study introduces Orga-Date, a lightweight lung organoid detection model based on YOLOv11n [27]. Orga-Date achieves an optimal trade-off among feature extraction capability, computational efficiency, and model complexity, while ensuring multi-scale adaptability and robustness in complex imaging scenarios. This study assessed the following aspects:  
To address defocus blur and multi-scale feature fusion challenges in microscopic imaging, we replace the original FPN + PAN structure in YOLOv11n with a BiFPN module. BiFPN employs cross-layer bidirectional connections and learnable weights to effectively differentiate organoids from background noise and imaging artifacts.