Multi-phase deep learning framework with Multiscale Adaptive Swin Transformer and embedding attention for precision lung nodule detection and classification

Abstract

Lung cancer remains a leading cause of cancer-related mortality worldwide, emphasizing the need for the accurate and efficient detection and classification of lung nodules. This study introduces an advanced multi-stage framework designed to address the challenges of precision, scalability, and adaptability in clinical diagnostics. This study presents a comprehensive framework for the detection, segmentation, and classification of lung nodules utilizing advanced preprocessing, segmentation, classification, and optimization techniques. The framework employs Sparse Edge-Preserving Enhancement (SEPE) for pre-processing, ensuring that critical nodule-specific features are retained while reducing noise. For segmentation, an enhanced DeepLabv3 + architecture integrates Atrous Spatial Pyramid Pooling (ASPP) and Refined Boundary Decoder (RBD) modules, supported by pretrained backbones, such as EfficientNetV2, DenseNet-201, ResNet-101, and InceptionV3. The classification phase leverages a Multiscale Adaptive Swin Transformer (MA-SwinT) with a Multi-Scale Embedding Attention Mechanism (MEAM) to accurately distinguish between benign and malignant nodules. Optimization using the Fossa Optimization Algorithm (FOA) fine-tunes the hyperparameters to ensure robust performance. The experimental results demonstrate the superiority of the framework on both the LUNA16 and LIDC-IDRI datasets. On the LUNA16 dataset, segmentation achieved a Dice Coefficient of 98.75%, IoU of 97.88%, Jaccard Index of 89.62%, and Hausdorff Distance of 2.025 mm, with an accuracy of 99.15%, precision of 98.50%, recall of 99.00%, F1 score of 98.75%, and specificity of 99.20%. For the LIDC-IDRI dataset, segmentation achieved a Dice Coefficient of 98.92%, IoU of 98.21%, Jaccard Index of 90.15%, and Hausdorff Distance of 2.010 mm, while the classification metrics achieved an accuracy of 99.40%, precision of 99.00%, recall of 99.20%, F1 score of 99.10%, and specificity of 99.55%. These results underline the ability of the framework to achieve high precision, recall, and overall accuracy, making it a reliable tool for lung nodule diagnosis in clinical applications.