Lung Nodule Characterization in CT Scans Using Hybrid 3D Attention U-Net Segmentation and Transfer Learning-Based Classification Approach (Comprehensive Journal of Science, 2025)
Distinguishing early between benign and malignant pulmonary nodules is essential fortimely and effective lung cancer care. In this study, we introduce a two-stage deep learning systemdesigned to automatically analyze lung nodules in CT scans. The first stage utilizes a 3D U-Netmodel enhanced with soft attention gates to accurately isolate nodule regions from the surroundinglung tissue. This approach takes full advantage of the 3D spatial context while minimizinginterference from unrelated anatomical features. To handle the challenge of imbalanced data wherenodules occupy only a small fraction of the scan train the segmentation model utilizing a combinedloss function that blends Dice loss with binary cross-entropy. In the second stage, the segmentednodules are fed into a classification pipeline built on EfficientNet-B7, adapted through transferlearning. The model analyzes 2D patches cropped from multiple axial slices of the segmentedvolume to categorize each nodule as normal, benign, or malignant. We evaluated our method on theIQ-OTH/NCCD lung cancer dataset and achieved a test accuracy of 95.2%, with both sensitivityand specificity surpassing 94% for detecting malignant cases. Our comparative evaluation shows aclear advantage over traditional methods that rely on manually engineered features, for instance, theGabor-GLCM combined with SVM, which achieved only 71.8% accuracy in our experiments. Bycombining attention-guided nodule segmentation with a transfer learning–based classifier, ourapproach captures richer, more discriminative features, leading to consistently reliable diagnosticoutcomes. Beyond performance gains, this study offers a practical, end-to-end pipeline that is bothreproducible and well-suited for clinical utilize particularly in settings with limited resources. Thedesign also reflects a growing emphasis in medical AI on systems that are not only accurate but alsoefficient and interpretable, supporting real-world clinical decision-making
Keywords: Lung cancer diagnosis, segmentation, U-Net, transfer learning, EfficientNet, CT image analysis, IQ-OTH/NCCD dataset.