Automated skin melanoma diagnostics based on mathematical model of artificial convolutional neural network

In the last 10 years there has been a revolu on in the fi eld of computer image analysis and pa ern recogni on. Modern algorithms of computer vision equaled and even in some problems surpassed human capabili es. This jerk is largely due to the emergence and development of the technology of deep convolu onal neural networks.

Recent developments in the fi eld of image processing and machine learning open up the prospect of crea ng systems based on ar fi cial neural convolu onal networks, superior to humans in problems of image classifi ca on, in par cular, in solving problems of analysis of various medical images. Among the most promising applica ons: automated recogni on and classifi ca on of skin diseases, detec on of pathologies on X-ray, CT, MRI, ultrasound imaging. In the proposed project, we will focusour a en on on the diagnosis of human skin diseases.

At the moment, melanoma is one of the most dangerous types of malignant tumors of the skin with a lot of deaths due to rapid metastasis, which is difficult to treat. The development of computer vision technology has allowed the development of technical vision systems that allow detec on and classifi ca on of skin diseases with a quality that is comparable and in some cases exceeds the values a ained by man.

In this paper, the authors propose an algorithm for the primary diagnosis of skin melanoma based on deep neural networks, achieving an accuracy of 91% for melanoma in dermatoscopic images. At the moment, the algorithm is implemented programma cally and is used in the test version of the online system for detec ng and classifying skin diseases, available at skincheckup.online.

Thanks to this development, the prospect of a signifi cantincrease in the propor on of people subjected to preven ve examina on for the presence of skin diseases opens up. Along with this, an addi onal source of informa on for specialized professionals can also play a role in seng the right diagnosis.

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