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...
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Nature Portfolio
2021
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oai:doaj.org-article:91858ca6bccc491fb77b60e3ff66f0dd2021-12-02T14:49:25ZA combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications10.1038/s41598-021-89352-82045-2322https://doaj.org/article/91858ca6bccc491fb77b60e3ff66f0dd2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89352-8https://doaj.org/toc/2045-2322Abstract 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.Hadi HashemzadehSeyedehsamaneh ShojaeilangariAbdollah AllahverdiMario RothbauerPeter ErtlHossein Naderi-ManeshNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021) |
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Medicine R Science Q Hadi Hashemzadeh Seyedehsamaneh Shojaeilangari Abdollah Allahverdi Mario Rothbauer Peter Ertl Hossein Naderi-Manesh A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications |
| 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 |
| 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 |
| title |
A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications |
| title_short |
A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications |
| title_full |
A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications |
| title_fullStr |
A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications |
| title_full_unstemmed |
A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications |
| title_sort |
combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/91858ca6bccc491fb77b60e3ff66f0dd |
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