MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans.
Lung cancer is one of the leading causes of cancer-related deaths worldwide. To reduce the mortality rate, early detection and proper treatment should be ensured. Computer-aided diagnosis methods analyze different modalities of medical images to increase diagnostic precision. In this paper, we propo...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0298527&type=printable |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850284852222361600 |
|---|---|
| author | Surya Majumder Nandita Gautam Abhishek Basu Arup Sau Zong Woo Geem Ram Sarkar |
| author_facet | Surya Majumder Nandita Gautam Abhishek Basu Arup Sau Zong Woo Geem Ram Sarkar |
| author_sort | Surya Majumder |
| collection | DOAJ |
| description | Lung cancer is one of the leading causes of cancer-related deaths worldwide. To reduce the mortality rate, early detection and proper treatment should be ensured. Computer-aided diagnosis methods analyze different modalities of medical images to increase diagnostic precision. In this paper, we propose an ensemble model, called the Mitscherlich function-based Ensemble Network (MENet), which combines the prediction probabilities obtained from three deep learning models, namely Xception, InceptionResNetV2, and MobileNetV2, to improve the accuracy of a lung cancer prediction model. The ensemble approach is based on the Mitscherlich function, which produces a fuzzy rank to combine the outputs of the said base classifiers. The proposed method is trained and tested on the two publicly available lung cancer datasets, namely Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) and LIDC-IDRI, both of these are computed tomography (CT) scan datasets. The obtained results in terms of some standard metrics show that the proposed method performs better than state-of-the-art methods. The codes for the proposed work are available at https://github.com/SuryaMajumder/MENet. |
| format | Article |
| id | doaj-art-07ab5ae8d6fb46a18e42bf2a03391583 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-07ab5ae8d6fb46a18e42bf2a033915832025-08-20T01:47:26ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-01193e029852710.1371/journal.pone.0298527MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans.Surya MajumderNandita GautamAbhishek BasuArup SauZong Woo GeemRam SarkarLung cancer is one of the leading causes of cancer-related deaths worldwide. To reduce the mortality rate, early detection and proper treatment should be ensured. Computer-aided diagnosis methods analyze different modalities of medical images to increase diagnostic precision. In this paper, we propose an ensemble model, called the Mitscherlich function-based Ensemble Network (MENet), which combines the prediction probabilities obtained from three deep learning models, namely Xception, InceptionResNetV2, and MobileNetV2, to improve the accuracy of a lung cancer prediction model. The ensemble approach is based on the Mitscherlich function, which produces a fuzzy rank to combine the outputs of the said base classifiers. The proposed method is trained and tested on the two publicly available lung cancer datasets, namely Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) and LIDC-IDRI, both of these are computed tomography (CT) scan datasets. The obtained results in terms of some standard metrics show that the proposed method performs better than state-of-the-art methods. The codes for the proposed work are available at https://github.com/SuryaMajumder/MENet.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0298527&type=printable |
| spellingShingle | Surya Majumder Nandita Gautam Abhishek Basu Arup Sau Zong Woo Geem Ram Sarkar MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans. PLoS ONE |
| title | MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans. |
| title_full | MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans. |
| title_fullStr | MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans. |
| title_full_unstemmed | MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans. |
| title_short | MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans. |
| title_sort | menet a mitscherlich function based ensemble of cnn models to classify lung cancer using ct scans |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0298527&type=printable |
| work_keys_str_mv | AT suryamajumder menetamitscherlichfunctionbasedensembleofcnnmodelstoclassifylungcancerusingctscans AT nanditagautam menetamitscherlichfunctionbasedensembleofcnnmodelstoclassifylungcancerusingctscans AT abhishekbasu menetamitscherlichfunctionbasedensembleofcnnmodelstoclassifylungcancerusingctscans AT arupsau menetamitscherlichfunctionbasedensembleofcnnmodelstoclassifylungcancerusingctscans AT zongwoogeem menetamitscherlichfunctionbasedensembleofcnnmodelstoclassifylungcancerusingctscans AT ramsarkar menetamitscherlichfunctionbasedensembleofcnnmodelstoclassifylungcancerusingctscans |