Design of a Lung Lesion Target Detection Algorithm Based on a Domain-Adaptive Neural Network Model

This study developed a novel domain-adaptive neural network framework, CNDAD-Net, for addressing the challenges of lung lesion detection in cross-domain medical image analysis. The proposed framework integrates domain adaptation techniques into a classical encoding-decoding structure to align feature distributions between source and target domains. Specifically, a "Generative Adversarial Network" GAN-based domain discriminator is utilized for the iterative refinement of feature representations to minimize cross-domain discrepancies and improve the generalization capability of the model. In addition, a novel Cross-Fusion Block (CFB) is proposed to implement multi-scale feature fusion that facilitates the deep integration of 2D, 3D, and domain-adapted features. The CFB achieves bidirectional feature flow across dimensions, thereby improving the model's capability to detect diverse lesion morphologies while minimizing false positives and missed detections. For better detection, coarse-grained domain adaptation is implemented by MMD for further optimization. It integrates a module inspired by a CycleGAN for the process to generate high-resolution images on low-quality data. Using the Lung Nodule Analysis (LUNA16) dataset, the test was conducted and its experimental result was compared with that of previous standard methods such as Faster R-CNN and YOLO, yielding mAP 0.889, recall at 0.845 and the F1-score at 0.886. This work, with a novel CNDAD-Net model, lays down a solid and scalable framework for the precise detection of lung lesions, which is extremely critical for early diagnosis and treatment. The model has prospects and is capable of being extended in future to multimodal imaging data ad real-time diagnostic scenarios, and can help in further developing intelligent medical image analysis systems.