Title: Deep learning-based lung cancer detection using CT images

Authors: Suguna Mariappan; Diana Moses

Addresses: Department of Information Technology, Thiagarajar College of Engineering, Madurai, India ' Department of Computer Science and Engineering, Methodist College of Engineering and Technology, King Koti Road, Abids, Hyderabad, 500001, Telangana, India

Abstract: This work demonstrates a hybrid deep learning (DL) model for lung cancer (LC) detection using CT images. Firstly, the input image is passed to the pre-processing stage, where the input image is filtered using a BF and the obtained filtered image is subjected to lung lobe segmentation, where segmentation is done using squeeze U-SegNet. Feature extraction is performed, where features including entropy with fuzzy local binary patterns (EFLBP), local optimal oriented pattern (LOOP), and grey level co-occurrence matrix (GLCM) features are mined. After completing the extracting of features, LC is detected utilising the hybrid efficient-ShuffleNet (HES-Net) method, wherein the HES-Net is established by the incorporation of EfficientNet and ShuffleNet. The presented HES-Net for LC detection is investigated for its performance concerning TNR, and TPR, and accuracy is established to have acquired values of 92.1%, 93.1%, and 91.3%.

Keywords: computed tomography; bilateral filter; squeeze U-SegNet; EfficientNet; ShuffleNet.

DOI: 10.1504/IJAHUC.2024.142162

International Journal of Ad Hoc and Ubiquitous Computing, 2024 Vol.47 No.3, pp.143 - 157

Received: 19 Mar 2024
Accepted: 30 May 2024
Published online: 10 Oct 2024 *

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