Open Access

ARTICLE

Deep Learning Enabled Computer Aided Diagnosis Model for Lung Cancer using Biomedical CT Images

by Mohammad Alamgeer¹, Hanan Abdullah Mengash², Radwa Marzouk², Mohamed K Nour³, Anwer Mustafa Hilal^4,*, Abdelwahed Motwakel⁴, Abu Sarwar Zamani⁴, Mohammed Rizwanullah⁴

1 Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
2 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
3 Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Saudi Arabia
4 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Anwer Mustafa Hilal. Email:

Computers, Materials & Continua 2022, 73(1), 1437-1448. https://doi.org/10.32604/cmc.2022.027896

Received 27 January 2022; Accepted 08 March 2022; Issue published 18 May 2022

Abstract

Early detection of lung cancer can help for improving the survival rate of the patients. Biomedical imaging tools such as computed tomography (CT) image was utilized to the proper identification and positioning of lung cancer. The recently developed deep learning (DL) models can be employed for the effectual identification and classification of diseases. This article introduces novel deep learning enabled CAD technique for lung cancer using biomedical CT image, named DLCADLC-BCT technique. The proposed DLCADLC-BCT technique intends for detecting and classifying lung cancer using CT images. The proposed DLCADLC-BCT technique initially uses gray level co-occurrence matrix (GLCM) model for feature extraction. Also, long short term memory (LSTM) model was applied for classifying the existence of lung cancer in the CT images. Moreover, moth swarm optimization (MSO) algorithm is employed to optimally choose the hyperparameters of the LSTM model such as learning rate, batch size, and epoch count. For demonstrating the improved classifier results of the DLCADLC-BCT approach, a set of simulations were executed on benchmark dataset and the outcomes exhibited the supremacy of the DLCADLC-BCT technique over the recent approaches.

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