Raman spectral feature selection using ant colony optimization for breast cancer diagnosis.
Pathology as a common diagnostic test of cancer is an invasive, time-consuming, and partially subjective method. Therefore, optical techniques, especially Raman spectroscopy, have attracted the attention of cancer diagnosis researchers. However, as Raman spectra contain numerous peaks involved in mo...
| Published in: | Lasers in Medical Science Vol. 33; no. 8; pp. 1799 - 1807 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Nov2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132434003&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132434003 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02688921 O4P jtl: Lasers in Medical Science issn: 02688921 maglogo: N pubinfo: dt: Nov2018 vid: 33 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 132434003 132434003 NLM29862464 10.1007/s10103-018-2544-3 NLM29862464 132434003 ppf: 1799 ppct: 8 formats: tig: atl: Raman spectral feature selection using ant colony optimization for breast cancer diagnosis. aug: au: Fallahzadeh, Omid Dehghani-Bidgoli, Zohreh Assarian, Mohammad affil: Department of Electrical and Computer Engineering, Kashan Branch, Islamic Azad University, Kashan, Iran sug: subj: Spectrum Analysis, Raman Methods Breast Neoplasms Diagnosis Algorithms Female Female ab: Pathology as a common diagnostic test of cancer is an invasive, time-consuming, and partially subjective method. Therefore, optical techniques, especially Raman spectroscopy, have attracted the attention of cancer diagnosis researchers. However, as Raman spectra contain numerous peaks involved in molecular bounds of the sample, finding the best features related to cancerous changes can improve the accuracy of diagnosis in this method. The present research attempted to improve the power of Raman-based cancer diagnosis by finding the best Raman features using the ACO algorithm. In the present research, 49 spectra were measured from normal, benign, and cancerous breast tissue samples using a 785-nm micro-Raman system. After preprocessing for removal of noise and background fluorescence, the intensity of 12 important Raman bands of the biological samples was extracted as features of each spectrum. Then, the ACO algorithm was applied to find the optimum features for diagnosis. As the results demonstrated, by selecting five features, the classification accuracy of the normal, benign, and cancerous groups increased by 14% and reached 87.7%. ACO feature selection can improve the diagnostic accuracy of Raman-based diagnostic models. In the present study, features corresponding to ν(C-C) αhelix proline, valine (910-940), νs(C-C) skeletal lipids (1110-1130), and δ(CH2)/δ(CH3) proteins (1445-1460) were selected as the best features in cancer diagnosis. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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