Tumor Spread Estimation in lungs using Medical Image Segmentation based on Deep Neural Networks

The accurate segmentation of lung tumors from medical images is pivotal for precise diagnosis and treatment planning in oncology. This study proposes a novel approach for tumor spread estimation in lungs leveraging deep neural networks (DNNs) for medical image segmentation. The proposed method employs a multi-stage framework integrating convolutional neural networks (CNNs) for automatic segmentation of lung tumors from computed tomography (CT) scans. By incorporating both spatial and contextual information, our DNN architecture enhances the segmentation accuracy, overcoming challenges such as irregular tumor shapes and low contrast boundaries. Furthermore, we introduce a quantitative assessment mechanism for estimating tumor spread, enabling clinicians to delineate tumor boundaries and predict potential metastatic regions more effectively. This methodology demonstrates promise for precise tumor detection and localization in lung CT scans, critical for early diagnosis and treatment planning in lung cancer patients. The results highlight the potential of integrating deep learning techniques with advanced medical imaging tools to enhance accuracy and efficiency in tumor spread estimation for lung cancer diagnosis.