Lung Cancer Segmentation Using an Enhanced TransUNet++ Architecture
Anoop V.
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Jyothi Engineering College, Thrissur, India
DOI: 10.18287/JBPE26.12.010305
Abstract
Lung cancer is a life-threatening disease in which accurate staging of malignant nodules using computed tomography (CT) scans is critical for reducing mortality. Most existing approaches rely solely on deep learning models. This work proposes an accurate and computationally efficient hybrid deep learning framework for lung cancer analysis, integrating advanced preprocessing, feature extraction, and hybrid network architectures. The pipeline begins with preprocessing steps including resizing, normalization, edge detection, and median filtering to enhance image quality. Texture features are extracted using local binary patterns (LBP), while principal component analysis (PCA) is applied for dimensionality reduction. The optimized features are classified using an EfficientNet-B0 model. For precise segmentation, EfficientNet-B0 is embedded within a Transformer-based UNET++ (TransUNET++), enabling effective modeling of both local details and global contextual dependencies. Evaluated on a benchmark CT dataset, the proposed method achieved 98.58% accuracy, 98.47% sensitivity, 99.23% specificity, and 98.42% precision for classification, along with strong segmentation performance (99.53% Dice similarity coefficient, 98.56% Intersection over Union, 99.73% Hausdorff distance, 98.86% volumetric overlap error), demonstrating high spatial agreement with ground-truth masks.
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