Lung tumor analysis using a thrice novelty block classification approach

Soniya, S. L.; Raj, T. Ajith Bosco

doi:10.1007/s11760-023-02523-0

Cited by 2 publications

(1 citation statement)

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“…For the classification of pulmonary nodules, a Random forest algorithm was used and achieved 95.34% accuracy, 90.53% sensitivity, 97.26% specificity, and 0.99 of AUC. Soniya et al [17] developed an automatic framework consisting of five steps including image acquisition, image enhancement, segmentation, feature extraction, and classification. In image acquisition, a total number of 250 and 1450 lung CT scans were obtained from in-house clinical and publicly available Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) respectively.…”

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%