Open Access

ARTICLE

Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

Iftikhar Naseer^1,*, Tehreem Masood¹, Sheeraz Akram¹, Arfan Jaffar¹, Muhammad Rashid², Muhammad Amjad Iqbal³

1 Faculty of Computer Science and Information Technology, The Superior University, Lahore, 54000, Pakistan
2 Department of Computer Science, National University of Technology, Islamabad, 45000, Pakistan
3 Faculty of IT, University of Central Punjab, Lahore, 54100, Pakistan

* Corresponding Author: Iftikhar Naseer. Email:

Computers, Materials & Continua 2023, 74(1), 2039-2054. https://doi.org/10.32604/cmc.2023.032927

Received 02 June 2022; Accepted 05 July 2022; Issue published 22 September 2022

Abstract

Lung cancer is the most dangerous and death-causing disease indicated by the presence of pulmonary nodules in the lung. It is mostly caused by the instinctive growth of cells in the lung. Lung nodule detection has a significant role in detecting and screening lung cancer in Computed tomography (CT) scan images. Early detection plays an important role in the survival rate and treatment of lung cancer patients. Moreover, pulmonary nodule classification techniques based on the convolutional neural network can be used for the accurate and efficient detection of lung cancer. This work proposed an automatic nodule detection method in CT images based on modified AlexNet architecture and Support vector machine (SVM) algorithm namely LungNet-SVM. The proposed model consists of seven convolutional layers, three pooling layers, and two fully connected layers used to extract features. Support vector machine classifier is applied for the binary classification of nodules into benign and malignant. The experimental analysis is performed by using the publicly available benchmark dataset Lung nodule analysis 2016 (LUNA16). The proposed model has achieved 97.64% of accuracy, 96.37% of sensitivity, and 99.08% of specificity. A comparative analysis has been carried out between the proposed LungNet-SVM model and existing state-of-the-art approaches for the classification of lung cancer. The experimental results indicate that the proposed LungNet-SVM model achieved remarkable performance on a LUNA16 dataset in terms of accuracy.

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Keywords

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