Open Access

ARTICLE

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

Iftikhar Naseer^1,2, Tehreem Masood^1,2, Sheeraz Akram^3,*, Zulfiqar Ali⁴, Awais Ahmad³, Shafiq Ur Rehman³, Arfan Jaffar^1,2

1 Faculty of Computer Sciences & Information Technology, The Superior University, Lahore, 54000, Pakistan
2 Intelligent Data Visual Computing Research (IDVCR), Lahore, 54000, Pakistan
3 Information System Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia
4 School of Computer Science and Electronic Engineering (CSEE), University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK

* Corresponding Author: Sheeraz Akram. Email:

(This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)

Computers, Materials & Continua 2024, 79(3), 4963-4977. https://doi.org/10.32604/cmc.2024.050204

Received 30 January 2024; Accepted 07 May 2024; Issue published 20 June 2024

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.

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