Classification of skin cancer using convolutional neural networks analysis of Raman spectra

Bratchenko, Ivan А.; Bratchenko, Lyudmila A.; Khristoforova, Yulia А.; Moryatov, Alexander A.; Kozlov, Sergey V.; Zakharov, Valery P.

doi:10.1016/j.cmpb.2022.106755

Cited by 48 publications

(37 citation statements)

References 60 publications

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“…Though these assays have been effective in reducing transmission rate, it is imperative to develop individual-friendly biofluids such as saliva or serum for COVID-19 patients screening and diagnosis. Vibrational spectroscopy [16] , based on Fourier transform infrared (FTIR) spectroscopy [17] , [18] or Raman scattering spectroscopy [19] , [20] , [21] provides a detailed fingerprinting of a biological sample from its chemical composition. Diagnostic tools based on these technologies reveal the potential to revolutionize clinical systems leading to evolutionary patient outcome, more efficient public services and significant economic savings.…”

Section: Introductionmentioning

confidence: 99%

Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19

Zhao

Zhai

Shao

et al. 2023

Computer Methods and Programs in Biomedicine

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Section: Introductionmentioning

confidence: 99%

Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19

Zhao

Zhai

Shao

et al. 2023

Computer Methods and Programs in Biomedicine

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Section: Introductionmentioning

confidence: 99%

“…In our previous work [ 29 , 30 ], we performed an optical biopsy using Raman and autofluorescence (AF) spectroscopy to diagnose skin cancer. Raman spectroscopy has proved to be a sensitive research instrument in clinical practice for a number of purposes [ 24 , 27 , 30 , 31 ]. The proposed method [ 29 , 32 ] was able to classify skin neoplasms with a mean accuracy higher than the accuracy of general practitioners or trainees, and with comparable or less accuracy than trained dermatologists and experts.…”

Section: Introductionmentioning

confidence: 99%

Combination of Optical Biopsy with Patient Data for Improvement of Skin Tumor Identification

Khristoforova

Bratchenko

et al. 2022

Diagnostics

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In this study, patient data were combined with Raman and autofluorescence spectral parameters for more accurate identification of skin tumors. The spectral and patient data of skin tumors were classified by projection on latent structures and discriminant analysis. The importance of patient risk factors was determined using statistical improvement of ROC AUCs when spectral parameters were combined with risk factors. Gender, age and tumor localization were found significant for classification of malignant versus benign neoplasms, resulting in improvement of ROC AUCs from 0.610 to 0.818 (p < 0.05). To distinguish melanoma versus pigmented skin tumors, the same factors significantly improved ROC AUCs from 0.709 to 0.810 (p < 0.05) when analyzed together according to the spectral data, but insignificantly (p > 0.05) when analyzed individually. For classification of melanoma versus seborrheic keratosis, no statistical improvement of ROC AUC was observed when the patient data were added to the spectral data. In all three classification models, additional risk factors such as occupational hazards, family history, sun exposure, size, and personal history did not statistically improve the ROC AUCs. In summary, combined analysis of spectral and patient data can be significant for certain diagnostic tasks: patient data demonstrated the distribution of skin tumor incidence in different demographic groups, whereas tumors within each group were distinguished using the spectral differences.

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“…5 Our research team recently demonstrated the applicability of portable Raman spectroscopic system for the analysis of skin neoplasms. 6,7 Portable spec- Currently, patient routing from a neoplasm detection to diagnosis is a step-by-step process that may take a significant amount of time. 8 Utilizing the additional noninvasive diagnostic methods may reduce this time and improve the statistics of medical errors in the skin cancer diagnosis.…”

mentioning

confidence: 99%

Raman‐based optical biopsy shortens the clinical pathway of the patient: Example of pigmented skin neoplasm diagnosis

Bratchenko

Moryatov

Bratchenko

2023

Photoderm Photoimm Photomed

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Classification of skin cancer using convolutional neural networks analysis of Raman spectra

Cited by 48 publications

References 60 publications

Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19

Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19

Combination of Optical Biopsy with Patient Data for Improvement of Skin Tumor Identification

Raman‐based optical biopsy shortens the clinical pathway of the patient: Example of pigmented skin neoplasm diagnosis

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