Title: A deep learning approach for lung cancer classification and nodule identification using CT-images

Authors: Siva Satya Sreedhar Purilla; Ashok Reddy Kandula; Kandula Srikanth; Jonnalagadda V.N. Raju; Tedla Balaji; Sureshbabu Chandanapalli

Addresses: Department of Information Technology, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, Andhra Pradesh 521356, India ' Department of Artificial Intelligence and Data Science, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, Andhra Pradesh 521356, India ' Department of Computer Science and Engineering, Dhanekula Institute of Engineering and Technology, Ganguru, Vijayawada, Andhra Pradesh 521139, India ' Department of Information Technology, Dhanekula Institute of Engineering and Technology, Ganguru, Vijayawada, Andhra Pradesh 521139, India ' Department of Information Technology, Seshadri Rao Gudlavalleru Engineering College (Autonomous), Gudlavalleru, Andhra Pradesh 521356, India ' Department of Information Technology, Seshadri Rao Gudlavalleru Engineering College (Autonomous), Gudlavalleru, Andhra Pradesh 521356, India

Abstract: This work devises an efficient technique deep learning enabled hybrid Shepard convolutional Kronecker network (ShCKN) for lung cancer classification and nodule identification using computed tomography (CT)-images. Initially, the image input is taken from the specific database and the acquired images are fed into an image pre-processing unit, where the Laplacian filter removes unnecessary noise. Thereafter, segmentation of the lung lobe is performed using K-Net. Then, nodules are detected using grid-based schemes. After that, feature extraction is performed and essential features are extracted using entropy measures. Finally, lung cancer classification is accomplished by the devised ShCKN, which is obtained by combining Shepard convolutional neural networks (ShCNN) and deep kernel networks (DKN). The performance estimation of ShCKN is validated based on early devised approaches and performance measures; the ShCKN achieves accuracy, F-measure, precision, recall, and specificity at 92%, 92%, 91%, 94%, and 93%.

Keywords: lung cancer; lung cancer classification; computer tomography; CT; convolutional neural network; CNN; deep learning; DL.

DOI: 10.1504/IJAHUC.2025.147758

International Journal of Ad Hoc and Ubiquitous Computing, 2025 Vol.49 No.4, pp.233 - 250

Received: 27 Mar 2024
Accepted: 06 Sep 2024
Published online: 30 Jul 2025 *

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