A preliminary study on in-vitro lung cancer detection using e-nose technology
The existing clinical diagnostics for lung cancer are mostly based on physics, biochemical and imaging techniques. The use of electronic nose (E-nose) system to detect volatile organic compounds (VOCs) in lung cancer cells or exhaled air breath of a patient is expected to be able to classify dif...
| Main Authors: | , , , , , , , , , , |
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| Format: | Proceeding Paper |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/47326/ http://irep.iium.edu.my/47326/4/47326-cover.pdf |
| _version_ | 1848783125368274944 |
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| author | Thriumani , Reena Zakaria, Ammar Jeffree, Amanina Iymia Hishamuddin, NA Omar, Mohammad Iqbl Adom, Abdul Hamid M. Shakaff, Ali Yeon Kamarudin, Latifah Munirah Yusuf, Nurlisa Hashim, Yumi Zuhanis Has-Yun Mohamed Helmy, Khaled |
| author_facet | Thriumani , Reena Zakaria, Ammar Jeffree, Amanina Iymia Hishamuddin, NA Omar, Mohammad Iqbl Adom, Abdul Hamid M. Shakaff, Ali Yeon Kamarudin, Latifah Munirah Yusuf, Nurlisa Hashim, Yumi Zuhanis Has-Yun Mohamed Helmy, Khaled |
| author_sort | Thriumani , Reena |
| building | IIUM Repository |
| collection | Online Access |
| description | The existing clinical diagnostics for lung cancer are
mostly based on physics, biochemical and imaging techniques.
The use of electronic nose (E-nose) system to detect volatile
organic compounds (VOCs) in lung cancer cells or exhaled air
breath of a patient is expected to be able to classify different
volatile components leading to the diagnosis of lung cancer at an
early stage. In this preliminary study, a commercialized E-nose
consists of an array of 32 conducting polymer sensors (Cyranose
320) was used to detect and discriminate the VOCs emitted from
cancer cells which is A549 (lung cancer cell line) between MCF7
(breast cancer cell line). Blank medium was used to obtain
controlled value. The VOC profiles of each sample were
characterized using a classification algorithm called k-Nearest
Neighbors (KNN) to test and benchmark the performance of Enose
in identifying VOCs of lung cancer from different cancer
cell lines. The E-nose with KNN classifier was able to classify the
VOCs of lung cancer cell with over 90% successful accuracy in
30 seconds. This study can conclude that e-nose is capable to
rapidly discriminate volatile organic compounds of cancerous
cells which generated during cell growth. |
| first_indexed | 2025-11-14T16:16:23Z |
| format | Proceeding Paper |
| id | iium-47326 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T16:16:23Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-473262018-05-24T04:56:48Z http://irep.iium.edu.my/47326/ A preliminary study on in-vitro lung cancer detection using e-nose technology Thriumani , Reena Zakaria, Ammar Jeffree, Amanina Iymia Hishamuddin, NA Omar, Mohammad Iqbl Adom, Abdul Hamid M. Shakaff, Ali Yeon Kamarudin, Latifah Munirah Yusuf, Nurlisa Hashim, Yumi Zuhanis Has-Yun Mohamed Helmy, Khaled Q Science (General) The existing clinical diagnostics for lung cancer are mostly based on physics, biochemical and imaging techniques. The use of electronic nose (E-nose) system to detect volatile organic compounds (VOCs) in lung cancer cells or exhaled air breath of a patient is expected to be able to classify different volatile components leading to the diagnosis of lung cancer at an early stage. In this preliminary study, a commercialized E-nose consists of an array of 32 conducting polymer sensors (Cyranose 320) was used to detect and discriminate the VOCs emitted from cancer cells which is A549 (lung cancer cell line) between MCF7 (breast cancer cell line). Blank medium was used to obtain controlled value. The VOC profiles of each sample were characterized using a classification algorithm called k-Nearest Neighbors (KNN) to test and benchmark the performance of Enose in identifying VOCs of lung cancer from different cancer cell lines. The E-nose with KNN classifier was able to classify the VOCs of lung cancer cell with over 90% successful accuracy in 30 seconds. This study can conclude that e-nose is capable to rapidly discriminate volatile organic compounds of cancerous cells which generated during cell growth. 2014-11-28 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/47326/4/47326-cover.pdf Thriumani , Reena and Zakaria, Ammar and Jeffree, Amanina Iymia and Hishamuddin, NA and Omar, Mohammad Iqbl and Adom, Abdul Hamid and M. Shakaff, Ali Yeon and Kamarudin, Latifah Munirah and Yusuf, Nurlisa and Hashim, Yumi Zuhanis Has-Yun and Mohamed Helmy, Khaled (2014) A preliminary study on in-vitro lung cancer detection using e-nose technology. In: 4th IEEE International Conference on Control Systems, Computing and Engineering (ICCSE 2014), 28th-30th November 2014, Batu Ferringhi, Penang Malaysia. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7072789 |
| spellingShingle | Q Science (General) Thriumani , Reena Zakaria, Ammar Jeffree, Amanina Iymia Hishamuddin, NA Omar, Mohammad Iqbl Adom, Abdul Hamid M. Shakaff, Ali Yeon Kamarudin, Latifah Munirah Yusuf, Nurlisa Hashim, Yumi Zuhanis Has-Yun Mohamed Helmy, Khaled A preliminary study on in-vitro lung cancer detection using e-nose technology |
| title |
A preliminary study on in-vitro lung cancer detection using e-nose technology
|
| title_full |
A preliminary study on in-vitro lung cancer detection using e-nose technology
|
| title_fullStr |
A preliminary study on in-vitro lung cancer detection using e-nose technology
|
| title_full_unstemmed |
A preliminary study on in-vitro lung cancer detection using e-nose technology
|
| title_short |
A preliminary study on in-vitro lung cancer detection using e-nose technology
|
| title_sort | preliminary study on in-vitro lung cancer detection using e-nose technology |
| topic | Q Science (General) |
| url | http://irep.iium.edu.my/47326/ http://irep.iium.edu.my/47326/ http://irep.iium.edu.my/47326/4/47326-cover.pdf |