Naslov
Early Tumor Detection by Multiple Infrared Unsupervised Neural Nets Fusion

Autori
Szu, Harold ; Kopriva, Ivica ; Hoekstra, Phil ; Diakides, Nicholas ; Diakides, Mary ; Buss, James ; Lupo, Jasper ;

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the IEEE 25th Annual International Conference of the Engineering in Medicine and Biology Society / - : Institute of Electrical and Electronics Engineers (IEEE), 2003, 1133-1136

Skup
IEEE 25th Annual International Conference of the Engineering in Medicine and Biology Society

Mjesto i datum
Cancún, Meksiko, 17.09.2003. - 21.09.2003

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Unsupervised classification; Multispectral imaging; Neural networks; Early tumor detection;

Sažetak
The unsupervised classification algorithm called Lagrange Constraint Neural Network (LCNN) has been successfully applied to the sub-pixel multi-spectral remote sensing, [25]. Here, we apply the LCNN to the early breast cancer detection using two-color mid and long infrared images of the breast. This could be a new paradigm shift that enabled smart neural network algorithm to sort out the underlying malignant heat sources for physician diagnoses. The non-intrusive 2-color passive infrared imaging that could be repeated for record track with no radiation hazard seems to be alternative paradigm shift for the first-line screening against breast cancer. The sub-pixel super-resolution capability of the remote sensing is equivalent to the submilimeter scaling of the close-up breast imaging for the vascular and the angiogenesis effects. We demonstrate the potential benefit of the multi-color mid & long infrared imaging capable for detecting the abnormal under-skin thermal textures as well as stage-zero detection of the ductal carcinoma in situ.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika

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