Naseer, Iftikhar and Masood, Tehreem and Akram, Sheeraz and Ali, Zulfiqar and Ahmad, Awais and Rehman, Shafiq Ur and Jaffar, Arfan (2024) Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis. Computers, Materials and Continua, 79 (3). pp. 4963-4977. DOI https://doi.org/10.32604/cmc.2024.050204
Naseer, Iftikhar and Masood, Tehreem and Akram, Sheeraz and Ali, Zulfiqar and Ahmad, Awais and Rehman, Shafiq Ur and Jaffar, Arfan (2024) Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis. Computers, Materials and Continua, 79 (3). pp. 4963-4977. DOI https://doi.org/10.32604/cmc.2024.050204
Naseer, Iftikhar and Masood, Tehreem and Akram, Sheeraz and Ali, Zulfiqar and Ahmad, Awais and Rehman, Shafiq Ur and Jaffar, Arfan (2024) Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis. Computers, Materials and Continua, 79 (3). pp. 4963-4977. DOI https://doi.org/10.32604/cmc.2024.050204
Abstract
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.
Item Type: | Article |
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Uncontrolled Keywords: | Lung cancer; segmentation; AlexNet; U -Net; classification |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 11 Oct 2024 18:54 |
Last Modified: | 30 Oct 2024 21:13 |
URI: | http://repository.essex.ac.uk/id/eprint/39396 |
Available files
Filename: TSP_CMC_50204.pdf
Licence: Creative Commons: Attribution 4.0