Developing lung cancer post-diagnosis system using pervasive data analytic framework

Pethuraj, Mohamed Shakeel; Aboobaider, Burhanuddin bin Mohd; Salahuddin, Lizawati

doi:10.1016/j.compeleceng.2022.108528

Cited by 3 publications

(1 citation statement)

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“…The researchers [20] developed a framework for lung cancer diagnosis consisting of three phases: Data segregation, feature extraction, and prediction. The data segregation step was done by utilizing Butterfly optimization, second features extractions and correlation were executed by Jaya optimization and finally, auto encoder algorithm was utilized for the prediction of lung tumor.…”

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%