• V. M. Lovkin National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine, Ukraine
  • S. A. Subbotin National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine, Ukraine
  • A. O. Oliinyk National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine, Ukraine
  • N. V. Myronenko National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine, Ukraine



dermatoscopy, medical diagnosis, convolutional neural network, skin disease, ResNet50 model, software component model


Context. The problem of skin disease diagnosis was investigated in the paper. Its actuality is caused by the necessity of automation of at least advisory medical decision making. Such decisions are made in telemedicine, for instance, when skin disease diagnostics is performed under specific conditions. These conditions are specified by situations when data for analysis are collected but a qualified doctor has no possibility to process the data and to make a diagnosis decision based on it. The object of the study is a process of skin disease diagnosis.

Objective. The objective of the study is to develop a skin disease diagnosis method to automate making of advisory medical diagnosis decisions and to increase efficiency of such decisions.

Method. The skin disease diagnosis method was proposed in the work. This method applies the modified ResNet50 model. It was proposed to add layers to the ResNet50 model and to train it using transfer learning and fine-tuning techniques. The method also defines image processing in particular through the change of its resolution and uses oversampling technique to prepare a dataset for model training.

Results. Experimental investigation of the proposed method was performed using the HAM10000 dataset which contains images of skin diseases. The images were collected using dermatoscopy method. The dataset contains observations for 7 different skin diseases. The proposed method is characterized by the accuracy of 96.31% on this dataset. It is improved accuracy in comparison with the existing neural network models. Software component model was created to give a possibility to integrate the proposed method into a medical diagnosis system.

Conclusions. The obtained results of the investigation suggest application of the proposed skin disease method in medical diagnostic system to make advisory decisions by the system and to support making final decisions by a doctor.

Author Biographies

V. M. Lovkin, National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine

PhD, Associate Professor, Associate Professor of the Department of Software Tools

S. A. Subbotin, National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine

Dr. Sc., Professor, Head of the Department of Software Tools

A. O. Oliinyk, National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine

Dr. Sc., Professor, Professor of the Department of Software Tools

N. V. Myronenko, National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine

Student of the Department of Software Tools


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How to Cite

Lovkin, V. M., Subbotin, S. A., Oliinyk, A. O., & Myronenko, N. V. (2023). METHOD AND SOFTWARE COMPONENT MODEL FOR SKIN DISEASE DIAGNOSIS . Radio Electronics, Computer Science, Control, (1), 40.



Neuroinformatics and intelligent systems

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