The extremely pressing issue of breast cancer diagnosis, which remains one of the leading causes of mortality, requires innovative approaches to improve the accuracy and speed of cancer diagnosis.
The paper substantiates the relevance of the problem of creating a comprehensive software system for automatic diagnosis of breast cancer, which is an important step in improving the accuracy and efficiency of oncological diagnosis. An approach to the development of such a system is proposed, which includes modules for biomedical image segmentation, classification, identification of informative features, automatic diagnosis, image synthesis, data set management, neural network models managements, metrics, and user management. The functional requirements for the software were developed and its architecture was designed in accordance with the principle of single responsibility: each module performs a clearly defined function, which ensures flexibility, scalability, and ease of further expansion of the system. The implementation was carried out in the form of a modern web application using Next.js, FastAPI, PyTorch, MongoDB, OpenCV, and Mantine UI technologies. The client-server architecture with support for cloud infrastructure allows for efficient processing of large amounts of medical data, ensuring reliability, performance, and high availability of the system. The database structure was designed in the form of a logical UML class diagram, which ensures reliable information management, support for CRUD operations, and compliance with the requirements for maintaining the integrity and confidentiality of medical data. Experimental studies have shown the effectiveness of neural networks in biomedical image segmentation and classification tasks, in particular for cell isolation, marker evaluation, and cancer subtype identification. The results of the study are of practical value and can be implemented in the activities of medical institutions, as well as used for the further development of intelligent decision support systems in the field of digital medicine.
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