SegChaNet: A Novel Model for Lung Cancer Segmentation in CT Scans
Accurate lung tumor identification is crucial for radiation treatment planning. Due to the low contrast of the lung tumor in computed tomography (CT) images, segmentation of the tumor in CT images is challenging. This paper effectively integrates the U-Net with the channel attention module (CAM) to...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.1155/2022/1139587 |
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| _version_ | 1832565280864206848 |
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| author | Mehmet Akif Cifci |
| author_facet | Mehmet Akif Cifci |
| author_sort | Mehmet Akif Cifci |
| collection | DOAJ |
| description | Accurate lung tumor identification is crucial for radiation treatment planning. Due to the low contrast of the lung tumor in computed tomography (CT) images, segmentation of the tumor in CT images is challenging. This paper effectively integrates the U-Net with the channel attention module (CAM) to segment the malignant lung area from the surrounding chest region. The SegChaNet method encodes CT slices of the input lung into feature maps utilizing the trail of encoders. Finally, we explicitly developed a multiscale, dense-feature extraction module to extract multiscale features from the collection of encoded feature maps. We have identified the segmentation map of the lungs by employing the decoders and compared SegChaNet with the state-of-the-art. The model has learned the dense-feature extraction in lung abnormalities, while iterative downsampling followed by iterative upsampling causes the network to remain invariant to the size of the dense abnormality. Experimental results show that the proposed method is accurate and efficient and directly provides explicit lung regions in complex circumstances without postprocessing. |
| format | Article |
| id | doaj-art-0aafb41247854c76a5a444a832be7fc2 |
| institution | Kabale University |
| issn | 1754-2103 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Bionics and Biomechanics |
| spelling | doaj-art-0aafb41247854c76a5a444a832be7fc22025-02-03T01:08:46ZengWileyApplied Bionics and Biomechanics1754-21032022-01-01202210.1155/2022/1139587SegChaNet: A Novel Model for Lung Cancer Segmentation in CT ScansMehmet Akif Cifci0Dept. of Computer EngineeringAccurate lung tumor identification is crucial for radiation treatment planning. Due to the low contrast of the lung tumor in computed tomography (CT) images, segmentation of the tumor in CT images is challenging. This paper effectively integrates the U-Net with the channel attention module (CAM) to segment the malignant lung area from the surrounding chest region. The SegChaNet method encodes CT slices of the input lung into feature maps utilizing the trail of encoders. Finally, we explicitly developed a multiscale, dense-feature extraction module to extract multiscale features from the collection of encoded feature maps. We have identified the segmentation map of the lungs by employing the decoders and compared SegChaNet with the state-of-the-art. The model has learned the dense-feature extraction in lung abnormalities, while iterative downsampling followed by iterative upsampling causes the network to remain invariant to the size of the dense abnormality. Experimental results show that the proposed method is accurate and efficient and directly provides explicit lung regions in complex circumstances without postprocessing.http://dx.doi.org/10.1155/2022/1139587 |
| spellingShingle | Mehmet Akif Cifci SegChaNet: A Novel Model for Lung Cancer Segmentation in CT Scans Applied Bionics and Biomechanics |
| title | SegChaNet: A Novel Model for Lung Cancer Segmentation in CT Scans |
| title_full | SegChaNet: A Novel Model for Lung Cancer Segmentation in CT Scans |
| title_fullStr | SegChaNet: A Novel Model for Lung Cancer Segmentation in CT Scans |
| title_full_unstemmed | SegChaNet: A Novel Model for Lung Cancer Segmentation in CT Scans |
| title_short | SegChaNet: A Novel Model for Lung Cancer Segmentation in CT Scans |
| title_sort | segchanet a novel model for lung cancer segmentation in ct scans |
| url | http://dx.doi.org/10.1155/2022/1139587 |
| work_keys_str_mv | AT mehmetakifcifci segchanetanovelmodelforlungcancersegmentationinctscans |