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An Automatic Lung Nodule Classification System Based on Hybrid Transfer Learning Approach

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Abstract

Lung cancers are not only affecting the lung but also impair the respiratory mechanism. Worldwide, it becomes one of the high causes of mortality in humans. Several computer aided diagnosis (CAD) systems have been designed in recent years for diagnosis of several diseases. Early detection of lung cancer has become very important which can enhance survival chances among humans. The average rates of survival people with lung-cancer can rise from 14 to 49 percent if the diagnosed is taking place in time. Compare to X-ray, computed tomography (CT) is more operative as it involves multiple imaging approaches to support each other. In this study, we have used lung-patient CT scan images to classify the lung nodule into four classes, which are small-cell-carcinoma, adenocarcinoma, squamous-cell-carcinoma and large-cell-carcinoma. This study has proposed VGG networks integrated with support vector machine and random forest to create a hybrid algorithm that further helps in reducing computation complexity of classification. The lung nodules are classified using these hybrids algorithms with highest accuracy 98.70% and compared the results with existing methods.

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Authors and Affiliations

  1. Machine Vision and Intelligence Lab, Department of Computer Science and Engineering, National Institute of Technology Jamshedpur, Jamshedpur, 831014, India

    Trishna Saikia, Dilip Kumar & Koushlendra Kumar Singh

  2. School of Electronics Engineering, Vellore Institute of Technology, Chennai Campus, Chennai, 600127, India

    Ranjeet Kumar

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  1. Trishna Saikia
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Correspondence to Koushlendra Kumar Singh.

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This article is part of the topical collection “Advances in Machine Vision and Augmented Intelligence” guest edited by Manish Kumar Bajpai, Ranjeet Kumar, Koushlendra Kumar Singh and George Giakos.

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Saikia, T., Kumar, R., Kumar, D. et al. An Automatic Lung Nodule Classification System Based on Hybrid Transfer Learning Approach. SN COMPUT. SCI. 3, 272 (2022). https://doi.org/10.1007/s42979-022-01167-0

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