COVID-19 Detection from Computed Tomography Images Using Slice Processing Techniques and a Modified Xception Classifier
This paper extends our previous method for COVID-19 diagnosis, proposing an enhanced solution for detecting COVID-19 from computed tomography (CT) images using a lean transfer learning-based model. To decrease model misclassifications, two key steps of image processing were employed. Firstly, the up...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2024/9962839 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849405324519276544 |
|---|---|
| author | Kenan Morani Esra Kaya Ayana Dimitrios Kollias Devrim Unay |
| author_facet | Kenan Morani Esra Kaya Ayana Dimitrios Kollias Devrim Unay |
| author_sort | Kenan Morani |
| collection | DOAJ |
| description | This paper extends our previous method for COVID-19 diagnosis, proposing an enhanced solution for detecting COVID-19 from computed tomography (CT) images using a lean transfer learning-based model. To decrease model misclassifications, two key steps of image processing were employed. Firstly, the uppermost and lowermost slices were removed, preserving sixty percent of each patient’s slices. Secondly, all slices underwent manual cropping to emphasize the lung areas. Subsequently, resized CT scans (224×224) were input into an Xception transfer learning model with a modified output. Both Xception’s architecture and pretrained weights were leveraged in the method. A big and rigorously annotated database of CT images was used to verify the method. The number of patients/subjects in the dataset is more than 5000, and the number and shape of the slices in each CT scan varies greatly. Verification was made both on the validation partition and on the test partition of unseen images. Results on the COV19-CT database showcased not only improvement from our previous solution and the baseline but also comparable performance to the highest-achieving methods on the same dataset. Further validation studies could explore the scalability and adaptability of the developed methodologies across diverse healthcare settings and patient populations. Additionally, investigating the integration of advanced image processing techniques, such as automated region of interest detection and segmentation algorithms, could enhance the efficiency and accuracy of COVID-19 diagnosis. |
| format | Article |
| id | doaj-art-535f282decae491eafc637ede4a38ecb |
| institution | Kabale University |
| issn | 1687-4196 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-535f282decae491eafc637ede4a38ecb2025-08-20T03:36:42ZengWileyInternational Journal of Biomedical Imaging1687-41962024-01-01202410.1155/2024/9962839COVID-19 Detection from Computed Tomography Images Using Slice Processing Techniques and a Modified Xception ClassifierKenan Morani0Esra Kaya Ayana1Dimitrios Kollias2Devrim Unay3Izmir Democracy UniversityYildiz Technical UniversityQueen Mary University of LondonIzmir Democracy UniversityThis paper extends our previous method for COVID-19 diagnosis, proposing an enhanced solution for detecting COVID-19 from computed tomography (CT) images using a lean transfer learning-based model. To decrease model misclassifications, two key steps of image processing were employed. Firstly, the uppermost and lowermost slices were removed, preserving sixty percent of each patient’s slices. Secondly, all slices underwent manual cropping to emphasize the lung areas. Subsequently, resized CT scans (224×224) were input into an Xception transfer learning model with a modified output. Both Xception’s architecture and pretrained weights were leveraged in the method. A big and rigorously annotated database of CT images was used to verify the method. The number of patients/subjects in the dataset is more than 5000, and the number and shape of the slices in each CT scan varies greatly. Verification was made both on the validation partition and on the test partition of unseen images. Results on the COV19-CT database showcased not only improvement from our previous solution and the baseline but also comparable performance to the highest-achieving methods on the same dataset. Further validation studies could explore the scalability and adaptability of the developed methodologies across diverse healthcare settings and patient populations. Additionally, investigating the integration of advanced image processing techniques, such as automated region of interest detection and segmentation algorithms, could enhance the efficiency and accuracy of COVID-19 diagnosis.http://dx.doi.org/10.1155/2024/9962839 |
| spellingShingle | Kenan Morani Esra Kaya Ayana Dimitrios Kollias Devrim Unay COVID-19 Detection from Computed Tomography Images Using Slice Processing Techniques and a Modified Xception Classifier International Journal of Biomedical Imaging |
| title | COVID-19 Detection from Computed Tomography Images Using Slice Processing Techniques and a Modified Xception Classifier |
| title_full | COVID-19 Detection from Computed Tomography Images Using Slice Processing Techniques and a Modified Xception Classifier |
| title_fullStr | COVID-19 Detection from Computed Tomography Images Using Slice Processing Techniques and a Modified Xception Classifier |
| title_full_unstemmed | COVID-19 Detection from Computed Tomography Images Using Slice Processing Techniques and a Modified Xception Classifier |
| title_short | COVID-19 Detection from Computed Tomography Images Using Slice Processing Techniques and a Modified Xception Classifier |
| title_sort | covid 19 detection from computed tomography images using slice processing techniques and a modified xception classifier |
| url | http://dx.doi.org/10.1155/2024/9962839 |
| work_keys_str_mv | AT kenanmorani covid19detectionfromcomputedtomographyimagesusingsliceprocessingtechniquesandamodifiedxceptionclassifier AT esrakayaayana covid19detectionfromcomputedtomographyimagesusingsliceprocessingtechniquesandamodifiedxceptionclassifier AT dimitrioskollias covid19detectionfromcomputedtomographyimagesusingsliceprocessingtechniquesandamodifiedxceptionclassifier AT devrimunay covid19detectionfromcomputedtomographyimagesusingsliceprocessingtechniquesandamodifiedxceptionclassifier |