Support Vector Machine on Fluorescence Landscapes for Breast Cancer Diagnostics

被引：16

作者：

Dramicanin, Tatjana ^{[1
]}

Lenhardt, Lea ^{[1
]}

Zekovic, Ivana ^{[1
]}

Dramicanin, Miroslav D. ^{[1
]}

机构：

[1] Univ Belgrade, Vinca Inst Nucl Sci, Belgrade 11001, Serbia

来源：

JOURNAL OF FLUORESCENCE | 2012年 / 22卷 / 05期

关键词：

Breast cancer; Fluorescence; Support vector machine; Synchronous fluorescence; SYNCHRONOUS LUMINESCENCE SPECTROSCOPY; IN-VIVO; RECOGNITION; TISSUES; DISCRIMINATION;

D O I：

10.1007/s10895-012-1070-0

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Excitation-emission matrices (EEM) and total synchronous fluorescence spectra (SFS) of normal and malignant breast tissue specimens are measured in UV-VIS spectral region to serve as data inputs in development of Support Vector Machine (SVM) based breast cancer diagnostics tool. Various input data combinations are tested for classification accuracy using SVM prediction against histopathology findings to discover the best combination regarding diagnostics sensitivity and specificity. It is shown that with EEM data SVM provided 67 % sensitivity and 62 % specificity diagnostics. With SFS data SVM provided 100 % sensitivity and specificity for a several input data combinations. Among these combinations those that require minimal data inputs are identified.

引用

页码：1281 / 1289

页数：9

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