Title: Pulmonary lung nodule detection and classification through image enhancement and deep learning

Authors: Nuthanakanti Bhaskar; Tumkur Sureshkumar Ganashree; Raj Kumar Patra

Addresses: CSE Department, VTU-RRC, Visvesvaraya Technological University, Belagavi, India; CMR Technical Campus, Hyderabad, Telangana, India ' Department of Telecommunication Engineering, Dayanandasagar College of Engineering, Bangalore, India ' Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaadeswaram, Andhra Pradesh, 522302, India

Abstract: In the medical image capturing process, the noise will be added in images and to analyse these images, proper enhancement and pre-process is required. Most of the researchers considered the same on CT lung images by using ROI selection, morphological operations, histogram equalisation, and binary thresholding methods, and they achieved around 95% of accuracy. To get better accuracy in the pre-processing stage of this present work resampling, morphological closure and image denoising techniques have been applied. In the image segmentation stage: for labelled nodule regions, the LIDC dataset is used, and for cancer/non-cancer labels, the KDSB17 dataset is used. In the segmentation stage: the U-net model has been applied. To minimise false positives of detected nodules, CNN is applied which converges to an 84.4% validation accuracy. The AUC of the CNN model was 0.6231, with a validation loss of 0.5646 and accuracy of 96%.

Keywords: image enhancement; deep learning; lung cancer; U-net; CNN.

DOI: 10.1504/IJBM.2023.130637

International Journal of Biometrics, 2023 Vol.15 No.3/4, pp.291 - 313

Received: 14 Jul 2021
Accepted: 25 Sep 2021
Published online: 02 May 2023 *

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