Abstract-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.
I.INTRODUCTIONEarly detection of the breast cancer should result in the increased survival probability. Decades data seemed to breed statistical controversial of the X-ray mammogram [19]. On the other hand, the cost-effective thermal infrared imaging in single spectral band has been routinely applied [21,22] and established screening tool in breast cancer [20] in developed countries in Europe as well as in developing countries in Asia due to the fear of the X-ray radiation hazard. In this paper, we applied brain-like smart neural network algorithm that demanded an eye-like pair of IR sensors. To promote both convenient and effective diagnoses, we recommend application of the smart subpixel super-resolution multiple spectral remote sensing algorithm called Lagrange Constraint neural Network (LCNN) [23][24][25] [18], the acceptance of passive IR imaging in the US was not comparable with the active Xray mammography. This was surprising from the current consensus standpoint regarding vascular-related tumor initiation and growth that provided a plausible explanation for the IR finding. In 1993, Head and Elliott [12] improved images of the high resolution second-generation IR systems provided objective and quantitative analysis, and reported that the growth-rate-related prognostic indicators were strongly associated with the IR results [13]. In 1996, Gamagami [14] studied angiogenesis by IR imaging and reported that hypervascularity and hyperthermia could be shown in 86% of non-palpable breast cancers. He also noted that in 15% of these cases, this technique helped to detect cancers that were not visible through mammography. The concept of angiogenesis, suggested by Gamagami as an integral part of early breast cancer, was reiterated in 1996 by Guido and Schnitt [15], whose observations suggested that angiogenesis is an early event in the development of breast cancer. They noted that it might occur before tumor cells acquired the ability to invade the surrounding stoma and even...
Carbon nanotubes (CNT) have a potential to be efficient infrared (IR) detection materials due to their unique electronic properties. The ballistic electronic transport property makes the noise equivalent temperature difference smaller compared to other semi-conducting materials. By overlaying CNT-based mid-IR (3-5µm) detectors on a long-wave IR (8-15µm) focal plane array, the mid-IR detector causes no filters loss. In order to verify this approach, a single pixel CNT-based infrared photodetector is fabricated by depositing the CNTs on the substrate surface and then aligning them using the atomic force microscopy (AFM)-based nanomanipulation system. Functionality of the single pixel CNT infrared detector is then verified and dark current is analyzed experimentally.
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