Title: Automated system-based classification of lung cancer using machine learning

Authors: Vidhi Bishnoi; Nidhi Goel; Akash Tayal

Addresses: Department of Electronics and Communication, Indira Gandhi Delhi Technical University for Women, Delhi, India ' Department of Electronics and Communication, Indira Gandhi Delhi Technical University for Women, Delhi, India ' Department of Electronics and Communication, Indira Gandhi Delhi Technical University for Women, Delhi, India

Abstract: Lung malignant growth is the well-known reason for death identified due to cancer worldwide. Therefore, to help the radiologist to detect it correctly, automated computer techniques come up with several machine learning classification. For such an automated technique, machine learning algorithms have been applied for the classification of CT scan lung images. This includes two proposed novel features Gabor energy, Gabor entropy, and five grey level co-occurrence matrix (GLCM). These new features are distinct and help in boosting the performance of the classifier to achieve higher accuracy. The proposed method has been simulated on 450 CT scan lung images acquired from the publicly available Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) dataset. As a result, the accuracy of 100%, 99%, 83%, and 92% have been achieved from support vector machine (SVM), neural networks (NN), Naive Bayes (NB), and perceptron, respectively.

Keywords: image processing; lung cancer; CT images; Lung Image Database Consortium; LIDC; machine learning; Gabor filter; Gabor energy; Gabor entropy.

DOI: 10.1504/IJMEI.2023.133130

International Journal of Medical Engineering and Informatics, 2023 Vol.15 No.5, pp.403 - 415

Received: 21 Jan 2021
Accepted: 05 Jun 2021
Published online: 01 Sep 2023 *

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