Multidimensional Analysis of Dermoscopic Images and Spectral Information for the Diagnosis of Skin Tumors

Irina A. Matveeva (Login required)
Samara National Research University, Russia

Andrey I. Komlev
Samara National Research University, Russia

Oleg I. Kaganov
Samara State Medical University, Russia
Samara Regional Clinical Oncology Dispensary, Russia

Alexander A. Moryatov
Samara State Medical University, Russia
Samara Regional Clinical Oncology Dispensary, Russia

Valery P. Zakharov
Samara National Research University, Russia


Paper #9079 received 12 Mar 2024; revised manuscript received 20 Mar 2024; accepted for publication 21 Mar 2024; published online 22 Mar 2024.

DOI: 10.18287/JBPE24.10.010307

Abstract

The paper is devoted to the identification of skin tumors and interpretation of their component composition based on multidimensional analysis of Raman scattering spectral data and dermoscopic images. The dataset contains 65 samples of malignant melanomas, 107 seborrheic keratoses, and 166 nevi. The multivariate curve resolution alternating least squares analysis was performed for the Raman spectra to extract spectral profiles of main skin components and their composition. The obtained biochemical profiles of skin neoplasms were analyzed by the gradient boosting method. Dermoscopic image analysis was performed using a convolutional neural network with modified Visual Geometry Group 16-layer model architecture. Joint analysis of component and spatial features was carried out using logistic regression of the predicted values of a model based on Raman spectra and a model based on image analysis. As a result, a binary model for the classification of malignant melanoma and pigmented benign neoplasms was constructed, showing an area under the receiver operating characteristic of 0.94 (0.90−0.98) with 95% confidence interval. Combining spatial and Raman spectra derived component features makes it possible to increase the efficiency of diagnosing skin cancer.

Keywords

Raman spectra; multivariate curve resolution; dermatoscopy; malignant melanoma; seborrheic keratosis; pigmented nevus; multivariate method; gradient boosting; convolutional neural network

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