Leveraging multi-scale feature integration in UNet and FPN for semantic segmentation of lung nodules.

Introduction: Lung cancer remains as an important source of cancer-related mortality worldwide, demonstrating a substantial challenge to public health systems. The absence of evident symptoms in the early stages makes timely diagnosis of lung cancer challenging. Early identification and treatment will reduce the mortality rate caused by lung cancer. Abnormal growths identified as lung or pulmonary nodules can be found in the lungs and some of these could be malignant. A Computer-Aided Detection (CAD) framework can aid in identifying pulmonary nodules by investigating medical images. Automated CAD systems assist radiologists by reducing the diagnostic workload and increasing the possibility of early lung cancer identification. Finding and accurately outlining lung nodules is the specific task of lung nodule segmentation in medical image analysis. Methods: Multi-scale UNet, Feature Pyramid Network (FPN) with Linear Attention Mechanism and UNet with Asynchronous Convolution Blocks (ACB) and Channel Attention Mechanism were used to segment lung nodules. Multi-scale UNet improvises the traditional UNet architecture by incorporating multi-scale convolutional operations, which improves feature extraction and boosts segmentation accuracy. The UNet with ACB and Channel Attention Mechanism employs a cross-like receptive field that can reduce the impact of redundant information in obtaining representative characteristics. FPN with Linear Attention mechanism uses a multi-scale feature pyramid to identify nodules of different sizes and a linear attention mechanism is employed to improve feature extraction. FPN with Linear Attention mechanism attains a linear time and spatial complexity while effectively segmenting pulmonary nodules. Results and discussion: Employing the FPN with Linear Attention mechanism yielded the highest performance in the experiments. The highest results in the study using FPN with Linear Attention were achieved using GELU on the LIDC-IDRI dataset with a DSC of 71.59% and IoU of 58.57%. The smooth, probabilistic weighting of GeLU complements the model's attention mechanisms.