DeepJoint Segmentation-based Lung Segmentation and Hybrid Optimization-Enabled Deep Learning for Lung Nodule Classification

P. Chinniah; Balajee Maram; P. VelrajkumarCh. Vidyadhari

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DeepJoint Segmentation-based Lung Segmentation and Hybrid Optimization-Enabled Deep Learning for Lung Nodule Classification

机译：DeepJoint Segmentation-based Lung Segmentation and Hybrid Optimization-Enabled Deep Learning for Lung Nodule Classification

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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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《International journal of pattern recognition and artificial intelligence》 |2022年第13期|2252021.1-2252021.29|共29页
作者
P. Chinniah; Balajee Maram; P. VelrajkumarCh. Vidyadhari;
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作者单位

Chitkara University School of Engineering and Technology, Chitkara University, Baddi, Himachal Pradesh,India;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
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Computed tomography images; DeepJoint model; Deep Residual Network; grid-based scheme; lung nodule classification;

DeepJoint Segmentation-based Lung Segmentation and Hybrid Optimization-Enabled Deep Learning for Lung Nodule Classification

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