"Deep Joint Segmentation for Lung Segmentation and Hybrid Optimization for Nodule Classification"

What is it about?

Lung cancer is an aggressive disease among all cancer-based diseases, because of causing huge mortality in humans. Thus, earlier discovery is a basic task for diagnosing lung cancer and it helps increase the survival rate. Computed tomography (CT) is a powerful imaging technique used to discover lung cancer. However, it is time-consuming for examining each CT image. This paper develops an optimized deep model for classifying the lung nodules. Here, the pre-processing is done using Region of Interest (ROI) extraction and adaptive Wiener filter. The segmentation is done using the DeepJoint model wherein distance is computed with a congruence coefficient for extracting the segments. The nodule identification is done by a grid-based scheme. The features such as Global Binary Pattern (GBP), Texton features, statistical features, perimeter and area, barycenter difference, number of slices, short axis and long axis and volume are considered. The lung nodule classification is done to classify part solid, solid nodules and ground-glass opacity (GGO) using Deep Residual Network (DRN), which is trained by the proposed Shuffled Shepard Sine–Cosine Algorithm (SSSCA). The developed SSSCA is generated by the integration of the Sine–Cosine Algorithm (SCA) and Shuffled Shepard Optimization Algorithm (SSOA). The proposed SSSCA-based DRN outperformed with the highest testing accuracy of 92.5%, sensitivity of 93.2%, specificity of 83.7% and F1 -score of 81.5%.

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Why is it important?

Lung cancer is characterized as an aggressive disease with high mortality rates. Therefore, early detection becomes paramount in improving patient outcomes. Early discovery of lung cancer significantly increases the chances of successful treatment and improves survival rates. This underscores the importance of developing efficient diagnostic methods. CT imaging is a powerful technique for detecting lung cancer. However, manually examining each CT image can be time-consuming, necessitating the development of automated methods for analysis. The passage introduces the development of an optimized deep learning model for classifying lung nodules. This suggests a shift towards leveraging advanced technologies, such as deep learning, to enhance the efficiency and accuracy of lung cancer diagnosis.

Perspectives

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It describes the development of an optimized deep learning model for classifying lung nodules. It outlines various steps involved in the process, including pre-processing, segmentation, nodule identification, and feature extraction, indicating a comprehensive approach to addressing the problem. By employing a Deep Residual Network (DRN), the research leverages deep learning techniques to classify lung nodules. This reflects the recognition of the potential of deep learning in medical image analysis tasks, particularly in complex and nuanced areas such as lung cancer diagnosis.

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