RETRACTED ARTICLE: A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications
Abstract Lung cancer is a leading cause of cancer death in both men and women worldwide. The high mortality rate in lung cancer is in part due to late-stage diagnostics as well as spread of cancer-cells to organs and tissues by metastasis. Automated lung cancer detection and its sub-types classifica...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-05-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-89352-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849234915507306496 |
|---|---|
| author | Hadi Hashemzadeh Seyedehsamaneh Shojaeilangari Abdollah Allahverdi Mario Rothbauer Peter Ertl Hossein Naderi-Manesh |
| author_facet | Hadi Hashemzadeh Seyedehsamaneh Shojaeilangari Abdollah Allahverdi Mario Rothbauer Peter Ertl Hossein Naderi-Manesh |
| author_sort | Hadi Hashemzadeh |
| collection | DOAJ |
| description | Abstract Lung cancer is a leading cause of cancer death in both men and women worldwide. The high mortality rate in lung cancer is in part due to late-stage diagnostics as well as spread of cancer-cells to organs and tissues by metastasis. Automated lung cancer detection and its sub-types classification from cell’s images play a crucial role toward an early-stage cancer prognosis and more individualized therapy. The rapid development of machine learning techniques, especially deep learning algorithms, has attracted much interest in its application to medical image problems. In this study, to develop a reliable Computer-Aided Diagnosis (CAD) system for accurately distinguishing between cancer and healthy cells, we grew popular Non-Small Lung Cancer lines in a microfluidic chip followed by staining with Phalloidin and images were obtained by using an IX-81 inverted Olympus fluorescence microscope. We designed and tested a deep learning image analysis workflow for classification of lung cancer cell-line images into six classes, including five different cancer cell-lines (P-C9, SK-LU-1, H-1975, A-427, and A-549) and normal cell-line (16-HBE). Our results demonstrate that ResNet18, a residual learning convolutional neural network, is an efficient and promising method for lung cancer cell-lines categorization with a classification accuracy of 98.37% and F1-score of 97.29%. Our proposed workflow is also able to successfully distinguish normal versus cancerous cell-lines with a remarkable average accuracy of 99.77% and F1-score of 99.87%. The proposed CAD system completely eliminates the need for extensive user intervention, enabling the processing of large amounts of image data with robust and highly accurate results. |
| format | Article |
| id | doaj-art-91858ca6bccc491fb77b60e3ff66f0dd |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2021-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-91858ca6bccc491fb77b60e3ff66f0dd2025-08-20T04:02:56ZengNature PortfolioScientific Reports2045-23222021-05-0111111010.1038/s41598-021-89352-8RETRACTED ARTICLE: A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applicationsHadi Hashemzadeh0Seyedehsamaneh Shojaeilangari1Abdollah Allahverdi2Mario Rothbauer3Peter Ertl4Hossein Naderi-Manesh5Nanobiotechnology Department, Faculty of Biosciences, Tarbiat Modares UniversityBiomedical Engineering Group, Department of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology (IROST)Biophysics Department, Faculty of Biosciences, Tarbiat Modares UniversityKarl Chiari Lab for Orthopaedic Biology, Department of Orthopedics and Trauma Surgery, Medical University of ViennaInstitute of Applied Synthetic Chemistry and Institute of Chemical Technologies and Analytics, Faculty of Technical Chemistry, Vienna University of TechnologyNanobiotechnology Department, Faculty of Biosciences, Tarbiat Modares UniversityAbstract Lung cancer is a leading cause of cancer death in both men and women worldwide. The high mortality rate in lung cancer is in part due to late-stage diagnostics as well as spread of cancer-cells to organs and tissues by metastasis. Automated lung cancer detection and its sub-types classification from cell’s images play a crucial role toward an early-stage cancer prognosis and more individualized therapy. The rapid development of machine learning techniques, especially deep learning algorithms, has attracted much interest in its application to medical image problems. In this study, to develop a reliable Computer-Aided Diagnosis (CAD) system for accurately distinguishing between cancer and healthy cells, we grew popular Non-Small Lung Cancer lines in a microfluidic chip followed by staining with Phalloidin and images were obtained by using an IX-81 inverted Olympus fluorescence microscope. We designed and tested a deep learning image analysis workflow for classification of lung cancer cell-line images into six classes, including five different cancer cell-lines (P-C9, SK-LU-1, H-1975, A-427, and A-549) and normal cell-line (16-HBE). Our results demonstrate that ResNet18, a residual learning convolutional neural network, is an efficient and promising method for lung cancer cell-lines categorization with a classification accuracy of 98.37% and F1-score of 97.29%. Our proposed workflow is also able to successfully distinguish normal versus cancerous cell-lines with a remarkable average accuracy of 99.77% and F1-score of 99.87%. The proposed CAD system completely eliminates the need for extensive user intervention, enabling the processing of large amounts of image data with robust and highly accurate results.https://doi.org/10.1038/s41598-021-89352-8 |
| spellingShingle | Hadi Hashemzadeh Seyedehsamaneh Shojaeilangari Abdollah Allahverdi Mario Rothbauer Peter Ertl Hossein Naderi-Manesh RETRACTED ARTICLE: A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications Scientific Reports |
| title | RETRACTED ARTICLE: A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications |
| title_full | RETRACTED ARTICLE: A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications |
| title_fullStr | RETRACTED ARTICLE: A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications |
| title_full_unstemmed | RETRACTED ARTICLE: A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications |
| title_short | RETRACTED ARTICLE: A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications |
| title_sort | retracted article a combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications |
| url | https://doi.org/10.1038/s41598-021-89352-8 |
| work_keys_str_mv | AT hadihashemzadeh retractedarticleacombinedmicrofluidicdeeplearningapproachforlungcancercellhighthroughputscreeningtowardautomaticcancerscreeningapplications AT seyedehsamanehshojaeilangari retractedarticleacombinedmicrofluidicdeeplearningapproachforlungcancercellhighthroughputscreeningtowardautomaticcancerscreeningapplications AT abdollahallahverdi retractedarticleacombinedmicrofluidicdeeplearningapproachforlungcancercellhighthroughputscreeningtowardautomaticcancerscreeningapplications AT mariorothbauer retractedarticleacombinedmicrofluidicdeeplearningapproachforlungcancercellhighthroughputscreeningtowardautomaticcancerscreeningapplications AT peterertl retractedarticleacombinedmicrofluidicdeeplearningapproachforlungcancercellhighthroughputscreeningtowardautomaticcancerscreeningapplications AT hosseinnaderimanesh retractedarticleacombinedmicrofluidicdeeplearningapproachforlungcancercellhighthroughputscreeningtowardautomaticcancerscreeningapplications |