Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation
A rapidly spreading epidemic, COVID-19 had a serious effect on millions and took many lives. Therefore, for individuals with COVID-19, early discovery is essential for halting the infection’s progress. To quickly and accurately diagnose COVID-19, imaging modalities, including computed tomography (CT...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
|
| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/9/2/42 |
| _version_ | 1797620156823764992 |
|---|---|
| author | Hassan Ali Khan Xueqing Gong Fenglin Bi Rashid Ali |
| author_facet | Hassan Ali Khan Xueqing Gong Fenglin Bi Rashid Ali |
| author_sort | Hassan Ali Khan |
| collection | DOAJ |
| description | A rapidly spreading epidemic, COVID-19 had a serious effect on millions and took many lives. Therefore, for individuals with COVID-19, early discovery is essential for halting the infection’s progress. To quickly and accurately diagnose COVID-19, imaging modalities, including computed tomography (CT) scans and chest X-ray radiographs, are frequently employed. The potential of artificial intelligence (AI) approaches further explored the creation of automated and precise COVID-19 detection systems. Scientists widely use deep learning techniques to identify coronavirus infection in lung imaging. In our paper, we developed a novel light CNN model architecture with watershed-based region-growing segmentation on Chest X-rays. Both CT scans and X-ray radiographs were employed along with 5-fold cross-validation. Compared to earlier state-of-the-art models, our model is lighter and outperformed the previous methods by achieving a mean accuracy of 98.8% on X-ray images and 98.6% on CT scans, predicting the rate of 0.99% and 0.97% for PPV (Positive predicted Value) and NPV (Negative predicted Value) rate of 0.98% and 0.99%, respectively. |
| first_indexed | 2024-03-11T08:36:50Z |
| format | Article |
| id | doaj.art-cf5f00232f82430da2e71c5875e4c0c3 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-11T08:36:50Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-cf5f00232f82430da2e71c5875e4c0c32023-11-16T21:25:12ZengMDPI AGJournal of Imaging2313-433X2023-02-01924210.3390/jimaging9020042Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing SegmentationHassan Ali Khan0Xueqing Gong1Fenglin Bi2Rashid Ali3Software Engineering Insitute, East China Normal University, Shanghai 200062, ChinaSoftware Engineering Insitute, East China Normal University, Shanghai 200062, ChinaSchool of Data Science and Engineering, East China Normal University, Shanghai 200062, ChinaSchool of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, ChinaA rapidly spreading epidemic, COVID-19 had a serious effect on millions and took many lives. Therefore, for individuals with COVID-19, early discovery is essential for halting the infection’s progress. To quickly and accurately diagnose COVID-19, imaging modalities, including computed tomography (CT) scans and chest X-ray radiographs, are frequently employed. The potential of artificial intelligence (AI) approaches further explored the creation of automated and precise COVID-19 detection systems. Scientists widely use deep learning techniques to identify coronavirus infection in lung imaging. In our paper, we developed a novel light CNN model architecture with watershed-based region-growing segmentation on Chest X-rays. Both CT scans and X-ray radiographs were employed along with 5-fold cross-validation. Compared to earlier state-of-the-art models, our model is lighter and outperformed the previous methods by achieving a mean accuracy of 98.8% on X-ray images and 98.6% on CT scans, predicting the rate of 0.99% and 0.97% for PPV (Positive predicted Value) and NPV (Negative predicted Value) rate of 0.98% and 0.99%, respectively.https://www.mdpi.com/2313-433X/9/2/42convolutional neural networkCOVID-19classificationCNNsegmentationwatershed segmentation |
| spellingShingle | Hassan Ali Khan Xueqing Gong Fenglin Bi Rashid Ali Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation Journal of Imaging convolutional neural network COVID-19 classification CNN segmentation watershed segmentation |
| title | Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation |
| title_full | Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation |
| title_fullStr | Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation |
| title_full_unstemmed | Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation |
| title_short | Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation |
| title_sort | novel light convolutional neural network for covid detection with watershed based region growing segmentation |
| topic | convolutional neural network COVID-19 classification CNN segmentation watershed segmentation |
| url | https://www.mdpi.com/2313-433X/9/2/42 |
| work_keys_str_mv | AT hassanalikhan novellightconvolutionalneuralnetworkforcoviddetectionwithwatershedbasedregiongrowingsegmentation AT xueqinggong novellightconvolutionalneuralnetworkforcoviddetectionwithwatershedbasedregiongrowingsegmentation AT fenglinbi novellightconvolutionalneuralnetworkforcoviddetectionwithwatershedbasedregiongrowingsegmentation AT rashidali novellightconvolutionalneuralnetworkforcoviddetectionwithwatershedbasedregiongrowingsegmentation |