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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Szu, H. Kopriva, I. Hoekstra, P. Diakides, N. Diakides, M. Buss, J. Lupo, J. |
| Copyright Year | 2003 |
| Description | Author affiliation: Dept. ECE, George Washington Univ., DC, USA (Szu, H.; Kopriva, I.) |
| Abstract | The unsupervised classification algorithm called Lagrange constraint neural network (LCNN) has been successfully applied to the sub-pixel multispectral 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 nonintrusive 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 sub-milimeter scaling of the close-up breast imaging for the vascular and the angiogenesis effects. We demonstrate the potential benefit of the multicolor 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. |
| Sponsorship | Whitaker Found |
| Starting Page | 1133 |
| Ending Page | 1136 |
| File Size | 392463 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780377893 |
| ISSN | 1094687X |
| DOI | 10.1109/IEMBS.2003.1279448 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-09-17 |
| Publisher Place | Mexico |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Tumors Infrared detectors Neural networks Infrared imaging Remote sensing Breast cancer Classification algorithms Lagrangian functions Cancer detection Hazards |
| Content Type | Text |
| Resource Type | Article |
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