Advanced Lung Nodule Staging through 3D-ResNet: Classifying CT Images for Enhanced Diagnostic Precision
Keywords:
3D Convolutional Neural Networks, CT Images, Lung Nodules, Lung Nodule Analysis, Lung Image Database ConsortiumAbstract
Lung nodules present a critical challenge in pulmonary diagnostics, necessitating accurate staging for optimal treatment planning. This study proposes an advanced approach employing 3DResNet for precise lung nodule staging via CT image classification. Leveraging the power of three-dimensional convolutional neural networks (3D CNNs), this methodology aims to enhance diagnostic precision by categorizing lung nodules into distinct stages based on radiological features extracted from CT images. The 3DResNet architecture enables robust feature learning by analyzing spatial relationships within volumetric data, facilitating the discrimination of subtle nodule characteristics indicative of different disease stages. The amalgamation of LIDC's (Lung Image Database Consortium) extensive annotated data and LUNA's (LUng Nodule Analysis) meticulously curated nodule annotations provides a robust foundation for training and validating the 3DResNet model. By capitalizing on this amalgamated dataset, the model endeavors to achieve heightened accuracy and generalization in classifying nodule stages, thus empowering clinicians with nuanced insights into disease progression. By training on a comprehensive dataset encompassing diverse nodule presentations and stages, the model endeavors to achieve superior accuracy in stage classification. The outcomes are expected to offer clinicians refined insights into disease progression, aiding in informed decision-making for personalized patient care and treatment strategies.
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