(untitled) (Comprehensive Journal of Science, 2025)
Distinguishing early between benign and malignant pulmonary nodules is essential for timely and effective lung cancer care. In this study, we introduce a two-stage deep learning system designed to automatically analyze lung nodules in CT scans. The first stage utilizes a 3D U-Net model enhanced with soft attention gates to accurately isolate nodule regions from the surrounding lung tissue. This approach takes full advantage of the 3D spatial context while minimizing interference from unrelated anatomical features. To handle the challenge of imbalanced data where nodules occupy only a small fraction of the scan train the segmentation model utilizing a combined loss function that blends Dice loss with binary cross-entropy. In the second stage, the segmented nodules are fed into a classification pipeline built on EfficientNet-B7, adapted through transfer learning. The model analyzes 2D patches cropped from multiple axial slices of the segmented volume to categorize each nodule as normal, benign, or malignant. We evaluated our method on the IQ-OTH/NCCD lung cancer dataset and achieved a test accuracy of 95.2%, with both sensitivity and specificity surpassing 94% for detecting malignant cases. Our comparative evaluation shows a clear advantage over traditional methods that rely on manually engineered features, for instance, the Gabor-GLCM combined with SVM, which achieved only 71.8% accuracy in our experiments. By combining attention-guided nodule segmentation with a transfer learning–based classifier, our approach captures richer, more discriminative features, leading to consistently reliable diagnostic outcomes. Beyond performance gains, this study offers a practical, end-to-end pipeline that is both reproducible and well-suited for clinical utilize particularly in settings with limited resources. The design also reflects a growing emphasis in medical AI on systems that are not only accurate but also efficient and interpretable, supporting real-world clinical decision-making