Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review.
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 experi...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 3; pp. 655 - 678 |
|---|---|
| Autores principales: | , , |
| Formato: | diagnostic images review tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2020
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143476541&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143476541 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2020 vid: 33 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143476541 143476541 143476541 10.1007/s10278-020-00320-6 143476541 ppf: 655 ppct: 23 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review. aug: au: Halder, Amitava Dey, Debangshu Sadhu, Anup K. affil: Computer Science and Engineering Department, Supreme Knowledge Foundation Group of Institutions, 712139, Hooghly, India sug: subj: Solitary Pulmonary Nodule Diagnosis Lung Neoplasms Diagnosis Deep Learning Radiography, Thoracic Methods Tomography, X-Ray Computed Methods Diagnosis, Computer Assisted Sensitivity and Specificity Radiologists Neural Networks (Computer) Early Detection of Cancer Decision Making, Clinical ab: 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. pubtype: Academic Journal doctype: diagnostic images review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|