Гібридна згорткова мережа для оброблення медичних зображень та виявлення раку молочної залози

• Date: 2022
• Main Authors: Zaychenko, Yuriy, Naderan, Maryam, Hamidov, Galib
• Format: Article
• Language: English
• Published: The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 2022
• Subjects: виявлення раку молочної залози; гібридна згорткова мережа; кодер; чутливість класифікації; зменшення розмірності
• Online Access: http://journal.iasa.kpi.ua/article/view/265629
• Journal Title: System research and information technologies

In this paper, the breast cancer detection problem using convolutional neural networks (CNN) is considered. The review of known works in this field is presented and analysed. Most of them rely only on feature extraction after the convolutions and use the precision of classification of malignant tumors as the main criterion. However, because of the huge number of parameters in the models, the time of computation is very large. A new structure of CNN is developed — a hybrid convolutional network consisting of convolutional encoder for features extraction and reduction of the complexity of the model and CNN for classification of tumors. As a result, it prevented overfitting the model and reduced training time. Further, while evaluating the performance of the convolutional model, it was suggested to consider recall and precision criteria instead of only accuracy like other works. The investigations of the suggested hybrid CNN were performed and compared with known results. After experiments, it was established the proposed hybrid convolutional network has shown high performance with sensitivity, precision, and accuracy of 93,50%, 91,60%, and 93%, respectively, and requires much less training time in the problem of breast cancer detection as compared with known works.