A robust semantic lung segmentation study for CNN-based COVID-19 diagnosis.
Chemometr Intell Lab Syst
; 231: 104695, 2022 Dec 15.
Article
in English
| MEDLINE | ID: covidwho-2238818
ABSTRACT
This paper aims to diagnose COVID-19 by using Chest X-Ray (CXR) scan images in a deep learning-based system. First of all, COVID-19 Chest X-Ray Dataset is used to segment the lung parts in CXR images semantically. DeepLabV3+ architecture is trained by using the masks of the lung parts in this dataset. The trained architecture is then fed with images in the COVID-19 Radiography Database. In order to improve the output images, some image preprocessing steps are applied. As a result, lung regions are successfully segmented from CXR images. The next step is feature extraction and classification. While features are extracted with modified AlexNet (mAlexNet), Support Vector Machine (SVM) is used for classification. As a result, 3-class data consisting of Normal, Viral Pneumonia and COVID-19 class are classified with 99.8% success. Classification results show that the proposed method is superior to previous state-of-the-art methods.
Full text:
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International databases
Database:
MEDLINE
Language:
English
Journal:
Chemometr Intell Lab Syst
Year:
2022
Document Type:
Article
Affiliation country:
J.chemolab.2022.104695
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