Overview

This focuses on the development of a deep learning-based framework for the segmentation and classification of lung nodules. The proposed method utilizes a combination of convolutional neural networks and transfer learning techniques to accurately identify the presence and location of nodules in medical images. The system is trained using a large dataset of annotated CT scans and validated using a separate set of images to ensure its accuracy and reliability. The results show that the deep learning approach achieves superior performance compared to traditional machine learning methods, with high accuracy in both segmentation and classification tasks. This research has significant implications for the early detection and treatment of lung cancer, as it provides a fast and accurate method for identifying and analyzing nodules in medical images. Overall, the study demonstrates the potential of deep learning-based approaches in medical image analysis and diagnosis.

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