Lung cancer is one of the most serious and common cancer types of today, with very uncomfortable and potentially cumbersome diagnostic techniques in x-ray, CT, CT-PET scans, bronchoscopies and biopsies. Completing all these steps can also take a long time and be time consuming for hospital staff. So finding a new safer and faster technique to diagnose cancer would be of great benefit. The objectives of this pilot study is to create an effective data storage system that can be scaled for larger data sets in a later study. The aim was also to see whether a E-nose can be used to find the differences in smell-prints from a healthy lung and a cancerous lung. As well as seeing if the E-nose can distinguish samples drawn from the lungs from exhaled air samples. Samples were taken on patients by the staff at ”Lung kliniken” at Link¨oping University Hospital during a bronchoscopy on patients with one-sided lung cancer. These samples were then analyzed by the E-nose which sensory response is later used to test the classification system that uses a mix of Principal Component Analysis (PCA) and K-Nearest Neighbour (KNN). Using a k = 7, the system was able to correctly classify 60 % of the samples when comparing cancerous and healthy lung samples. Comparing exhaled, healthy and cancerous samples the accuracy was calculated to 55.56 %. Comparing all lung samples against exhaled samples the accuracy was 86.67 %
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-129563 |
| Date | January 2016 |
| Creators | Bäckström, Martin |
| Publisher | Linköpings universitet, Institutionen för medicinsk teknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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