Lung-Net: A deep learning framework for lung tissue segmentation in three-dimensional thoracic CT images

Delfan, Niloufar; Moghaddam, Hamid Abrishami; Modaresi, Mohammadreza; Afshari, Kimia; Nezamabadi, Kasra; Pak, Neda; Ghaemi, Omid; Forouzanfar, Mohamad

doi:10.48550/arxiv.2212.13971

Cited by 1 publication

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“…Various computational intelligence-based approaches are widely used to segment and classify lung cancer. Delfan et al [10] presented a model that utilized a DL framework comprising of U-Net architecture incorporated with pre-trained InceptionV3 blocks for lung tissue segmentation and obtained 0.951 of dice coefficient on LUNA16 dataset. The authors in the study [11] employed deep learning techniques to segment lung parenchyma and identify lung nodules in CT scan images.…”

Section: Introductionmentioning

confidence: 99%

Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

Naseer,

Masood,

Akram

et al. 2024

CMC

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Lung cancer is a leading cause of global mortality rates. Early detection of pulmonary tumors can significantly enhance the survival rate of patients. Recently, various Computer-Aided Diagnostic (CAD) methods have been developed to enhance the detection of pulmonary nodules with high accuracy. Nevertheless, the existing methodologies cannot obtain a high level of specificity and sensitivity. The present study introduces a novel model for Lung Cancer Segmentation and Classification (LCSC), which incorporates two improved architectures, namely the improved U-Net architecture and the improved AlexNet architecture. The LCSC model comprises two distinct stages. The first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung lobes. Subsequently, an improved AlexNet architecture is employed to classify lung cancer. During the first stage, the proposed model demonstrates a dice accuracy of 0.855, a precision of 0.933, and a recall of 0.789 for the segmentation of candidate nodules. The suggested improved AlexNet architecture attains 97.06% accuracy, a true positive rate of 96.36%, a true negative rate of 97.77%, a positive predictive value of 97.74%, and a negative predictive value of 96.41% for classifying pulmonary cancer as either benign or malignant. The proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

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Section: Introductionmentioning

confidence: 99%