With the rapid development of deep learning technology, convolutional neural networks (CNNs) have achieved remarkable success in the field of medical image processing, promoting the application of various CNN-based object detection models in pulmonary nodule detection tasks. Among them, the YOLO series models have attracted widespread attention in pulmonary nodule detection research due to their ability to balance high accuracy and real-time performance. For example, the YOLOv5-CASP model proposed by Ji et al. (2023) enhances feature extraction and multi-scale fusion capabilities for small pulmonary nodules by introducing the CBAM attention mechanism, improving the ASPP module, and replacing standard convolutions with CoT modules, thereby significantly improving detection accuracy. However, the added modules also substantially increase computational complexity without fully considering model efficiency. The plug-and-play pulmonary nodule detection solution proposed by Tang et al. (2025) enhances the model’s perception of nodules of different sizes by constructing a multi-scale dual-branch attention mechanism, and designs a cross-layer aggregation module to mitigate detail loss during feature transmission, significantly improving the localization capability of small nodules while maintaining detection accuracy. The YOLO-MSRF model proposed by Wu et al. (2024) effectively improves the detection accuracy of small pulmonary nodules by introducing three key enhancements: a small-target detection layer, a multi-scale receptive field module, and efficient omnidirectional convolution.

Beyond the aforementioned end-to-end detection models, another widely adopted technical paradigm involves using pre-trained deep convolutional neural networks as feature extractors, combined with traditional machine learning classifiers for final diagnosis. For instance, Lanjewar et al. (2023) proposed a modified DenseNet201 model, which incorporates pooling and Dropout layers for a lightweight design to extract high-level features from CT images. These features are then refined using feature selection methods such as ETC and MRMR before being fed into various machine learning classifiers, reportedly achieving high classification accuracy on a specific dataset. Such hybrid approaches leverage the strengths of deep learning in feature representation and the high efficiency of classical machine learning algorithms. However, the feature extraction and classification stages are disconnected, preventing true end-to-end optimization. More importantly, their performance heavily depends on the effectiveness of feature selection, and they still suffer from issues such as feature loss and inadequate representation when dealing with morphologically complex and minute pulmonary nodules. Furthermore, most of these studies are validated on single, small-scale datasets, leaving their generalization capability and clinical interpretability insufficiently substantiated.

Although current research has made significant progress in improving the accuracy of pulmonary nodule detection, there remains a notable shortcoming in the synergistic optimization of accuracy and computational efficiency, making it difficult to meet the stringent computational resource requirements of clinical practice. Particularly given the characteristics of pulmonary nodule detection tasks—where small targets and multi-scale distribution coexist—existing methods still lack a comprehensive solution capable of simultaneously addressing both detection accuracy and operational efficiency.

Therefore, a major focus and ongoing challenge in current research is how to develop a detection model capable of achieving end-to-end collaborative optimization while possessing high detection accuracy, strong generalization capability, good clinical interpretability, and high computational efficiency. Bridging this gap is of significant importance for advancing the clinical adoption of intelligent pulmonary nodule detection technologies.

The Transformer architecture leverages self-attention mechanisms to capture global dependencies in parallel, overcoming limitations in long-range modeling. Its exceptional scalability has established it as a foundational technology in both natural language processing and computer vision. With ongoing technological evolution, Transformer-based approaches are gradually being applied in the medical field, demonstrating potential to surpass traditional methods, particularly in medical image analysis (Carion et al., 2020). To address the morphological and positional complexity of pulmonary nodules in medical images, researchers have begun introducing adaptable modules such as deformable convolutions into the DETR framework to enhance its perception and localization capabilities for irregular targets (Han et al., 2023). Tang et al. (2025) proposed an enhanced pulmonary nodule detection algorithm based on RT-DETR, named LN-DETR. The proposed algorithm improves cross-scale feature fusion via newly-designed deep and shallow detail fusion layers, optimizes the computational load of the backbone network to reduce model size, and enhances contextual information by using efficient downsampling methods. Zhou et al. (2025) proposed a Transformer-based pulmonary nodule detection model named LN-DETR, which innovatively integrates three core modules: the PC-EMA module enhances feature extraction capability while optimizing computational efficiency through multi-scale attention and partial convolution mechanisms; the GS-CCFM module facilitates effective cross-scale feature fusion using grouped shuffle convolution; and the CTrans module further improves overall feature fusion through cross-channel attention. This model significantly enhances computational efficiency while maintaining detection accuracy.

Building upon existing Transformer models, this study systematically addresses key issues in pulmonary nodule detection, such as inadequate multi-scale feature fusion and inefficient channel interaction, by designing multi-scale attention and cross-layer fusion modules, thereby achieving simultaneous improvements in detection accuracy and computational efficiency. However, the method still requires enhancement in capturing features of very small nodules, and its adaptability to nodules with complex morphologies needs further validation. More experiments are needed to support its practicality and robustness in real clinical environments.

To address the issue of insufficient feature representation capability in the baseline RT-DETR model for small target detection tasks, this study systematically improves the model from two dimensions: feature pyramid construction and cross-scale feature fusion. The existing model’s strategy of only fusing features from the P3-P5 levels results in the ineffective utilization of high-resolution features containing critical details. To overcome this limitation, this paper introduces a Space-to-Depth Convolution (SPDConv) module to perform deep enhancement on the P2-level features, which are rich in spatial details. By reorganizing spatial dimensional information into channel dimensions, this module expands the receptive field while maintaining the high spatial resolution of the feature maps, thereby enhancing semantic representation capability while preserving fine spatial structures and significantly improving the feature discriminability of small targets. The enhanced P2 features are fused into the P3 level through the lateral connection mechanism of the feature pyramid, enabling the model to obtain high-quality small target representations in the early stages of feature extraction.

In the field of medical image analysis, challenges such as blurred boundaries and significant scale differences between lesion areas and normal tissues often arise, manifested as the dilution of semantic information during fusion and the difficulty of fixed fusion coefficients in adapting to dynamically changing imaging scenarios. To address these challenges, this paper introduces a lightweight Modulation Fusion Module (MFM) (Deng et al., 2025), whose structure is illustrated in Figure 4. This module dynamically allocates weights to different input branches during the feature fusion process, enhancing the interaction capability among multi-scale features and thereby enabling efficient integration of cross-level semantic information. This method demonstrates strong adaptability and robustness in detecting low-resolution, small-scale targets such as micro-lesions in CT images. First, the module uses a 1 × 1 convolution to align the features of each branch with the target dimension C. Specifically, for the input feature map F i ∈ ℝ B × C i × H × W with C i input channels: If C i ≠ C, a single 1 × 1 convolution projects it into the C-dimensional space. Otherwise, an identity mapping is applied to preserve the original features and avoid redundant computations. All aligned feature maps are concatenated along the channel dimension to form the tensor F i ∈ ℝ B × C i × H × W . It is then reshaped into the tensor F ∈ ℝ B × n × C × H × W to preserve the independent semantics of each branch. Here, ℝ indicates that each element in the tensor belongs to the real number domain. This is the most common case, as feature values and computations in deep learning are typically based on floating-point numbers. B is Batch Size. Represents the number of samples (such as images) processed simultaneously in one forward pass. This is crucial for parallel computation and efficient training. C i is Input Channels. Denotes the depth of the feature map. For intermediate feature maps in the network, it represents the number of features extracted by that layer. H is Height. The number of pixels or feature points in the vertical direction of the feature map. W is Width. The number of pixels or feature points in the horizontal direction of the feature map.

For the input feature map F i ∈ ℝ B × C i × H × W , it is first projected to the target channel number C through a 1 × 1 convolution. The aligned features from all branches are concatenated along the channel dimension to form a tensor F ∈ ℝ B × ( n ∗ C ) × H × W , which is then reshaped into F ∈ ℝ B × n × C × H × W to preserve the semantics of each branch. Subsequently, the global contextual representation g is computed, as shown in Equation 3:

Here, GAP stands for Global Average Pooling, and g represents the aggregated global context representation.

A weight vector for each branch is generated by a two-layer MLP (performing dimensionality reduction followed by expansion). This facilitates the adaptive learning of the relative importance of features from each branch according to the current context. The detailed computation is provided in Equation 4.

Here, A represents the adaptive weight matrix. The Con v 1 × 1 down and up of Con v 1 × 1 up represent dimensionality reduction and dimensionality enhancement, respectively.

Finally, The MFM module achieves effective fusion of multi-scale semantics by performing a weighted summation of the weights A with the aligned features F , The fused feature F out is obtained through a weighted summation. As shown in Equation 5:

To enhance the accuracy and robustness of CT pulmonary nodule detection under constrained real-time computational budgets, this study redesigns the neck structure of the model, with a focus on improving two core components: upsampling and feature fusion (Li et al., 2024; Li, 2024). These improvements are integrated into a novel module termed the Rethinking Features-Fused-Pyramid-Neck (RFPN). Traditional methods exhibit notable limitations: nearest-neighbor upsampling, by simply replicating low-frequency semantic information and directly overlaying it with high-frequency shallow textures, tends to induce feature misalignment and noise amplification. Meanwhile, commonly used CBS modules suffer from weak channel interaction capabilities and inefficient receptive field expansion under limited computational budgets, thereby constraining the quality of multi-scale feature fusion.

To address the upsampling stage, this paper introduces a soft nearest-neighbor interpolation method, whose mathematical expression is given by Equation 6:

U S nearest is the nearest neighbor upsampling operation. X is the input feature map. f is the upsampling factor, and the scaling factor 1 f 2 is used to calibrate feature responses to avoid cross-layer feature imbalance.

This method applies a soft scaling factor related to the upsampling factor after upsampling, performing regional normalization on high-level semantic features to calibrate their feature responses in a smooth manner. This strategy effectively mitigates cross-layer feature imbalance without introducing any trainable parameters, significantly improving the recall rate and boundary segmentation quality of small-sized pulmonary nodules while preserving detailed textures. The structural design is illustrated in Figure 5.

During the feature fusion stage, this paper adopts the lightweight aggregation unit GSConvE-I, whose structure is illustrated in Figure 6. This module takes the output X from the previous stage as input and initially performs channel compression and alignment through a 1 × 1 convolution, as shown in Equation 7:

Subsequently, the feature flow is split into two parallel processing paths: one path preserves the main information flow, while the other sequentially extracts deep features through 3 × 3 standard convolution, depthwise convolution, and the GELU activation function, as shown in Equation 8:

Finally, cross-path information interaction is achieved through channel concatenation and shuffling, as illustrated in Equation 9:

Here, f DWConv represents depthwise separable convolution. Shuffle represents the channel shuffle operation.

This design significantly enhances channel interaction capability and effective receptive field while maintaining low computational complexity. It effectively suppresses feature aliasing and imaging artifacts, providing more discriminative multi-scale feature representations for the detection head.

In this study, to evaluate the effectiveness of the model, we utilized a widely recognized public dataset for pulmonary nodule detection: the Tianchi Pulmonary Nodule Dataset (Cloud, 2017). The data were divided into training set (70%), validation set (15%), and test set (15%). Stratified sampling was employed to ensure that key dimensions such as nodule size, pathological type, and imaging characteristics maintained distributions consistent with the original dataset. The partitioning was conducted at the patient level to ensure that slices from the same patient did not span across subsets, thereby preventing data leakage and enhancing reproducibility. To validate the model’s practical application value in real clinical environments, this study specifically selected CT imaging data from 50 confirmed pulmonary nodule patients randomly retrieved from the hospital PACS system, ensuring data accuracy and reliability.

Figure 7 illustrates the performance evolution of the PrecisionMicro-DETR model over 300 training epochs. The model achieved its best performance on the mAP₅₀ metric, rapidly converging to 0.9 and maintaining stability, demonstrating its outstanding capability in identifying positive samples.

The loss convergence characteristics of PrecisionMicro-DETR during the training process are shown in Figure 8. The curve indicates that the loss value declined rapidly in the initial training phase and then stabilized. The overall convergence trajectory is smooth and free of significant fluctuations, indicating a stable and well-optimized model training process.

Based on the analysis of ablation experiments, as shown in Table 1, the proposed PrecisionMicro-DETR architecture achieves synergistic optimization of detection accuracy and computational efficiency in the task of CT pulmonary nodule detection. Regarding the core metric mAP₅₀ for pulmonary nodule detection, the MFM module demonstrates a significant improvement in the localization accuracy of small nodules, increasing the baseline model’s performance from 0.932 to 0.947, thereby validating its effectiveness in enhancing feature representation capability within complex lung tissue backgrounds. The fully integrated PrecisionMicro-DETR further elevates mAP₅₀ to 0.949, highlighting the synergistic enhancement effect of multiple modules in pulmonary nodule detection.

From the perspective of comprehensive detection performance, the trend of F1-Score clearly reflects the continuous optimization of the model in the completeness of pulmonary nodule detection. The MFM module increases the F1 value from the baseline of 0.920–0.940, primarily due to its enhancement in the recall capability for small nodules, with recall rising from 0.884 to 0.904. When the three modules are fully integrated, the F1-Score peaks at 0.9468, while recall significantly improves to 0.942. These results indicate that the model effectively reduces the rate of missed detection of pulmonary nodules while maintaining high-precision recognition, which holds significant value for clinical diagnosis.

In this ablation experiment, the F1 metric, as the harmonic mean of precision and recall, systematically reveals the contribution of each module to the comprehensive performance of the model. The continuous improvement in the F1 score—from 0.920 of the baseline model to 0.946 of the fully integrated architecture—validates the effectiveness of the module integration strategy. Among these, the MFM module demonstrates the most significant individual improvement effect, elevating the F1 value to 0.9403, primarily due to its multi-scale feature fusion capability enhancing the detection of small pulmonary nodules. Notably, when all three modules are fully integrated, the F1 score reaches its peak of 0.9468, while recall improves to 0.942. This indicates that the model significantly enhances nodule detection ability while maintaining high precision, which is of great importance for reducing missed diagnosis rates in clinical practice.

The experimental data further reveal the synergistic mechanisms among the modules. The combination of MFM and RFPN yields superior results compared to other dual-module configurations, indicating functional complementarity between the two in feature fusion and feature pyramid optimization. In contrast, the relatively lower F1 score observed with the SSTF and MFM combination suggests that careful design is required for module compatibility. Regarding model efficiency, all improved schemes maintain steady growth in F1-Score while reducing the parameter count by over 35%, demonstrating that this optimization approach not only ensures model lightweighting but also enhances overall performance through more refined architectural design.

The experimental results of this study demonstrate that PrecisionMicro-DETR, through the organic integration of multiple modules, successfully overcomes the trade-off between accuracy and computational complexity that traditional models face in CT pulmonary nodule detection tasks. By significantly reducing model parameters while comprehensively improving detection performance, this characteristic provides a new technical pathway for developing clinically applicable pulmonary nodule-assisted diagnostic systems, holding substantial clinical application value.

Figure 9 visualizes the attention regions of the models in the ablation study through heatmaps, where warm tones (red) indicate high attention and cool tones (blue) indicate low attention. The analysis reveals that: (a) The attention of the baseline model RT-DETR-R34 is the most dispersed, with numerous highlighted yet non-lesion responses, indicating that its attention is easily distracted by surrounding tissues; (b) After introducing partial improvement modules, the model’s attention to lesion regions becomes more concentrated, but significant distracting responses still persist in the background, suggesting that the feature fusion and selection mechanisms are not yet fully developed; (c) The complete PrecisionMicro-DETR model (integrating modules such as SSTF and MFM) demonstrates highly focused attention on the lesion core and its edges, with background noise effectively suppressed. This demonstrates that the proposed multi-module collaborative mechanism can guide the model to allocate limited computational resources more precisely to discriminative lesion features, thereby achieving systematic improvement in detection performance while controlling computational complexity.

To comprehensively evaluate the performance of PrecisionMicro-DETR, this study conducted a horizontal comparison with multiple mainstream detection models. The baseline model in this study is RT-DETR-34. In the comparative experiments, the same dataset split and training parameters were adopted to ensure fairness. PrecisionMicro-DETR demonstrated comprehensive competitive advantages, with the experimental results summarized in Table 2.

Compared to the YOLO series, the proposed method significantly outperformed YOLOv8 (0.906), YOLOv9 (0.89), and YOLOv10 (0.878) in terms of the mAP₅₀ metric, while also surpassing YOLOv9, which has 60.79 million parameters, in computational efficiency. In comparison with traditional detection algorithms, PrecisionMicro-DETR achieved a 17.3% improvement over Faster R-CNN (0.809) and a 63.1% improvement over SSD (0.582) in mAP₅₀, highlighting the advantages of the Transformer-based detection framework in the field of medical imaging.

When compared to models within the same series, PrecisionMicro-DETR exhibited a unique balance of performance. Although RT-DETR R18 achieved a relatively high mAP_50-95 score of 0.763, its mAP₅₀ was 0.935, which is lower than the 0.949 achieved by the proposed method. More importantly, while maintaining high precision and a parameter count comparable to RT-DETR-R18, PrecisionMicro-DETR improved the recall rate from 0.916 to 0.942, which holds significant clinical importance for reducing the missed diagnosis rate of pulmonary nodules.

A comprehensive analysis indicates that PrecisionMicro-DETR successfully achieves a synergistic optimization of precision and efficiency. The overall improvement in key metrics such as mAP₅₀ and recall rate, combined with a substantial reduction in parameter count and computational load, demonstrates that the proposed architectural enhancements effectively address the challenge of balancing small target recognition and computational efficiency in medical image detection. Compared to existing mainstream algorithms, the proposed method significantly reduces computational resource requirements while maintaining competitive detection accuracy, offering a more viable solution for the clinical deployment of CT pulmonary nodule detection.

The value of this study lies in proposing a detection framework that balances precision and efficiency, providing new technical insights for the field of medical image analysis. Future work will focus on further optimizing the model structure and validating its generalization capability on more medical image datasets.

K-fold cross-validation systematically evaluates model performance across different data distributions by partitioning the dataset into multiple complementary subsets, providing more robust evaluation results compared to a single train-test split. This method maximizes the utilization of limited data resources while ensuring the independence of the test set, allowing each sample to participate once in testing and K-1 times in training—making it particularly suitable for application scenarios with limited data scales, such as medical imaging. The results of this K-fold validation are presented in Table 3, which indicates that Fold-1 achieved the best performance, with precision (95.2%), recall (94.2%), and mAP₅₀ (94.9%) all reaching high levels. In contrast, Fold-4 exhibited relatively weaker performance, with significant declines across all metrics. Although the PrecisionMicro-DETR model demonstrated outstanding performance under optimal conditions (e.g., Fold-1), its performance stability requires further improvement. The relatively poor performance of Fold-4 may be attributed to data quality issues or class imbalance, revealing potential problems in the model and providing direction for subsequent optimization.

To investigate the reasons for the relatively lower performance of Fold-4 (mAP 78.1%), this paper additionally trained several mainstream detection models as baseline comparisons on the same data split (i.e., Fold-4 of the Tianchi dataset), including YOLOv5, RT-DETR-R18, R34, and R50. All models were trained using identical train-validation-test splits and hyperparameter settings to ensure fair comparison. The experimental results, as shown in Table 4, indicate that the mAP of all compared models on Fold-4 is significantly lower than their respective average performance on other folds. Under this specific split, this paper’s model (78.1%) still consistently outperforms all compared models. These results strongly suggest that the performance fluctuation in Fold-4 is primarily due to the inherent challenges of this particular data subset (Fold-4), rather than unique shortcomings of our model. Further analysis of the dataset reveals that this fold may contain more small and indistinct nodules, or nodules distributed in more challenging locations (such as adjacent to the pleura or blood vessels), which inherently pose greater difficulties for any detection model.