Abstract
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.
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All the procedures performed in the study involving human participants were in accordance with the ethical standards of the Islamic Azad University Research Committee as well as with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
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Informed consent was obtained from all the participants included in the study.
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Fallahzadeh, O., Dehghani-Bidgoli, Z. & Assarian, M. Raman spectral feature selection using ant colony optimization for breast cancer diagnosis. Lasers Med Sci 33, 1799–1806 (2018). https://doi.org/10.1007/s10103-018-2544-3
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DOI: https://doi.org/10.1007/s10103-018-2544-3