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Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review

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Abstract

This paper presents a systematic review of the literature focused on the lung nodule detection in chest computed tomography (CT) images. Manual detection of lung nodules by the radiologist is a sequential and time-consuming process. The detection is subjective and depends on the radiologist’s experiences. Owing to the variation in shapes and appearances of a lung nodule, it is very difficult to identify the proper location of the nodule from a huge number of slices generated by the CT scanner. Small nodules (< 10 mm in diameter) may be missed by this manual detection process. Therefore, computer-aided diagnosis (CAD) system acts as a “second opinion” for the radiologists, by making final decision quickly with higher accuracy and greater confidence. The goal of this survey work is to present the current state of the artworks and their progress towards lung nodule detection to the researchers and readers in this domain. This review paper has covered the published works from 2009 to April 2018. Different nodule detection approaches are described elaborately in this work. Recently, it is observed that deep learning (DL)-based approaches are applied extensively for nodule detection and characterization. Therefore, emphasis has been given to convolutional neural network (CNN)-based DL approaches by describing different CNN-based networks.

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Authors and Affiliations

  1. Computer Science and Engineering Department, Supreme Knowledge Foundation Group of Institutions, Hooghly, 712139, India

    Amitava Halder

  2. Electrical Engineering Department, Jadavpur University, Kolkata, 700032, India

    Debangshu Dey

  3. EKO CT & MRI Scan Centre, Medical College and Hospitals Campus, Kolkata, 700073, India

    Anup K. Sadhu

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Halder, A., Dey, D. & Sadhu, A.K. Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review. J Digit Imaging 33, 655–677 (2020). https://doi.org/10.1007/s10278-020-00320-6

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