Title: Improved diagnosis of lung cancer classification based on deep learning method

Authors: Amel Feroui; Meriem Saim; Mohammed El Amine Lazouni; Sihem Amel Lazzouni; Zineb Aziza Elaouaber; Mahammed Messadi

Addresses: Biomedical Engineering Laboratory, Tlemcen University, Tlemcen, 13000, Algeria ' Biomedical Engineering Laboratory, Tlemcen University, Tlemcen, 13000, Algeria ' Biomedical Engineering Laboratory, Tlemcen University, Tlemcen, 13000, Algeria ' Biomedical Engineering Laboratory, Tlemcen University, Tlemcen, 13000, Algeria ' Biomedical Engineering Laboratory, Tlemcen University, Tlemcen, 13000, Algeria ' Biomedical Engineering Laboratory, Tlemcen University, Tlemcen, 13000, Algeria

Abstract: Lung cancer, a globally impactful and severe disease, affects millions worldwide. Uncontrolled growth of abnormal lung tissue cells results in severe complications and high mortality. Early detection is crucial for improved prognosis and survival rates. This study presents two methods for lung cancer classification utilising computed tomography (CT) images, which offer detailed scans of the lungs. The first method employs the VGG16 and VGG19 deep learning architectures. The second method utilises pre-trained VGG16 and VGG19 models for feature extraction, followed by training of supervised learning algorithms SVM, k-NN, and decision (DT) for classification. Evaluation of the proposed methods was conducted on two publicly available databases: the LIDC-IDRI database and the IQ-OTH/NCCD database. The results demonstrated that the VGG19 architecture outperforms the VGG16 architecture in terms of accuracy and precision across both databases. However, VGG16 excels on a hybrid database. Additionally, the k-NN classifier outperforms the SVM and decision tree classifiers, indicating the superiority of transfer learning over deep learning for lung cancer image classification. The proposed system has potential implications for improving patient outcomes through early detection and diagnosis.

Keywords: lung cancer; CT scan images; deep learning; machine learning; biomedical engineering; artificial intelligence; medical imaging; classification; LIDC-IDRI database; IQ-OTH/NCCD database.

DOI: 10.1504/IJBET.2024.141571

International Journal of Biomedical Engineering and Technology, 2024 Vol.46 No.2, pp.138 - 159

Received: 05 Dec 2023
Accepted: 02 Mar 2024
Published online: 24 Sep 2024 *

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