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A Survey on Convolutional Neural Network (Deep-Learning Technique) -Based Lung Cancer Detection

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Abstract

Lung cancer is a malignant disease caused due to over-consumption of tobacco and cigarettes. In lung region, the unmanageable growth of cells will affect the human’s survival rate. Detection of lung cancer at its earlier stage is done from proper demonstration using computer-aided diagnosis (CAD) techniques. Deep-learning methodology is an improved version of the artificial neural networks, which consists of several layers to generate high-order features from its input, and then, brings out the predicted value on the top of the network. Among deep-learning techniques, in particular, convolutional neural network (CNN) has been widely applied in computer vision tasks. Because of a greater number of computed tomography (CT) scan images, quick and precise detection of disease was difficult for radiologists. Hence, necessity of CAD for lung cancer detection was increasing, and therefore, deep-learning (DL) techniques were used for earlier detection of lung cancer. Recent advanced technique named 3-Dimensional CNN (3D CNN) mechanism is widely considered to be better for lung cancer detection than applying 2-Dimensional CNN (2D CNN).

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Authors and Affiliations

  1. Annamalai University, Chidambaram, Tamil Nadu, India

    Lakshmi Narayana Gumma

  2. Department of Computer Science Engineering, Annamalai University, Chidambaram, Tamil Nadu, India

    R. Thiruvengatanadhan & T. Sivaprakasam

  3. Department of Computer Science Engineering, Narasaraopeta Engineering College, Narasaraopet, India

    LakshmiNadh Kurakula

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  1. Lakshmi Narayana Gumma
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  2. R. Thiruvengatanadhan
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  3. LakshmiNadh Kurakula
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This article is part of the topical collection “Intelligent Systems” guest edited by Geetha Ganesan, Lalit Garg, Renu Dhir, Vijay Kumar and Manik Sharma.

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Gumma, L.N., Thiruvengatanadhan, R., Kurakula, L. et al. A Survey on Convolutional Neural Network (Deep-Learning Technique) -Based Lung Cancer Detection. SN COMPUT. SCI. 3, 66 (2022). https://doi.org/10.1007/s42979-021-00887-z

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