Name
Title | ||
|---|---|---|
Detection of cancer using advanced computerized analysis of infrared spectra of peripheral blood. |
Abstract | ||
|---|---|---|
We have developed a novel approach for detection of cancer based on biochemical analysis of peripheral blood plasma using Fourier transform infrared spectroscopy. This approach has proven to be quick, safe, minimal invasive, and effective. Our approach recognizes any signs of solid tumor presence, regardless of location in the body or cancer type by measuring a spectrum that gives information regarding the total molecular composition and structure of the peripheral blood samples. The analysis includes clinically relevant preprocessing and feature extraction with principal component analysis, and uses Fisher's linear discriminant analysis to classify between cancer patients and healthy controls. We evaluated our method with leave-one-out cross validation and were able to establish sensitivity of 93.33%, specificity of 87.8%, and overall accuracy of 90.7%. Using our method for cancer detection should result in fewer unnecessary invasive procedures and yield fast detection of solid tumors. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TBME.2012.2226882 | IEEE Trans. Biomed. Engineering |
Keywords | Field | DocType |
Fourier transform spectra,biochemistry,biomedical optical imaging,blood,cancer,feature extraction,image classification,infrared spectra,medical image processing,molecular biophysics,molecular configurations,principal component analysis,sensitivity,Fisher linear discriminant analysis,Fourier transform infrared spectroscopy,advanced computerized analysis,biochemical analysis,cancer detection,cancer patients,feature extraction,healthy controls,image classification,infrared spectra,leave-one-out cross validation,peripheral blood plasma,principal component analysis,sensitivity,solid tumor presence recognition,total molecular composition,total molecular structure,Cancer detection,Fisher’s linear discriminant analysis (FLDA),Fourier transform infrared (FTIR),plasma,principle component analysis (PCA) | Biomedical engineering,Peripheral,Computer science,Blood plasma,Artificial intelligence,Contextual image classification,Pathology,Computer vision,Feature extraction,Linear discriminant analysis,Cross-validation,Cancer,Principal component analysis | Journal |
Volume | Issue | ISSN |
60 | 2 | 1558-2531 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ela Ostrovsky | 1 | 0 | 0.34 |
| Udi Zelig | 2 | 0 | 0.34 |
| Irina Gusakova | 3 | 0 | 0.34 |
| Samuel Ariad | 4 | 0 | 0.34 |
| Shaul Mordechai | 5 | 0 | 0.34 |
| Ilana Nisky | 6 | 183 | 24.59 |
| Joseph Kapilushnik | 7 | 0 | 0.34 |