ArticleChemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society2022
A robust semantic lung segmentation study for CNN-based COVID-19 diagnosis.
Article in Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- EndoNet: A Multiscale Deep Learning Framework for Multiple Gastrointestinal Disease Classification via Endoscopic Images.Diagnostics (Basel, Switzerland) · 2025Article
- Determining Area Affected by Corona in Lung Computed Tomography Images by Three-phase Level Set and Shearlet Transform.Journal of medical signals and sensors · 2025Article
- PlaqueNet: deep learning enabled coronary artery plaque segmentation from coronary computed tomography angiography.Visual computing for industry, biomedicine, and art · 2024Article
- CNN-RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images.Sensors (Basel, Switzerland) · 2023Article
- A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods.Diagnostics (Basel, Switzerland) · 2022Article
- Multi-task localization of the hemidiaphragms and lung segmentation in portable chest X-ray images of COVID-19 patients.Digital healthArticle
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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.
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