The aim of the study: development of a screening method for patients aimed at early differential diagnosis of malignant skin neoplasms using dermatoscopy in combination with optoelectronic mobile equipment and algorithms for classifying dermatoscopic images based on machine learning methods.
Materials and methods. To implement the detection of malignant neoplasms and classify them into the appropriate nosological group, machine learning methods, algorithms and optical recognition are used. The latter is used in the process of forming dermatoscopic images and training classification algorithms and models. The machine learning approaches are multi-class and binary cascade two-stage classification methods by classification algorithms based on the visual transformer architecture and neural network architecture.
Results. During the experimental evaluation of the results of multi-class classification (eight types of malignant neoplasms), the best classification model with the visual transformer architecture was determined, characterized by the metrics Accuracy of 0.932 and F-measure of 0.891 on the formed dataset, including ISIC-2019 and our own set containing 657 images. The binary cascade two-stage classification for melanocytic and non-melanocytic neoplasms has Accuracy and F-measure values — of 0.954 and 0.948 (the first stage of classification) and for melanomas and nevi — 0.964 and 0.951, respectively (the second stage of classification).
Conclusion. Conclusion. The obtained quantitative values of the malignant skin neoplasms detection accuracy by the developed screening examination method allow us to recommend the introduction of a multi-class classification for the primary division of a large volume of dermatoscopic images patients by nosological sign between medical specialists in the process of conducting mass (visiting) preventive examinations, and the introduction of a cascade binary classification in the an initial appointment conditions with limited access to specialized specialists to differentiate melanoma from other skin neoplasms. The developed screening examination method for patients can be introduced into medical practice as a system for supporting physician decision-making