Lung Cancer Classification using Modified U-Net Based Lobe Segmentation and Nodule Detection
DOI:
https://doi.org/10.15662/IJEETR.2026.0802428Keywords:
Lung Cancer Classification, Modified U-Net, Lobe Segmentation, Nodule Detection, Medical Imaging, Radiomics, CNN, Lung CT ScansAbstract
Lung cancer remains a leading cause of mortality worldwide, emphasizing the critical need for early and accurate diagnosis. Advances in medical imaging and artificial intelligence have paved the way for automated methods to identify and classify lung cancer. This study proposes a novel framework combining a modified U-Net architecture for precise lobe segmentation with advanced nodule detection techniques to enhance the accuracy and reliability of lung cancer classification. The proposed algorithm incorporates multi-scale feature extraction and adaptive attention mechanisms to address the variability in lung anatomy and the complexity of nodule morphology. By leveraging these innovations, the system effectively segments the lung lobes, isolates suspicious nodules, and classifies cancerous regions with improved sensitivity and specificity. The lobe segmentation stage employs a modified U-Net architecture, incorporating residual connections and dilated convolutions to capture intricate anatomical details. This enhancement ensures robust segmentation, even in cases with significant variations in lung structure due to disease progression
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