Abstract:
This work is devoted to the research and development of methods for
effectively identifying breast pathologies using modern machine learning
technologies, such as you only look once (YOLOv8) and faster region-based
convolutional neural network (R-CNN). The paper presents an analysis of
existing approaches to the diagnosis of breast diseases and an assessment of
their effectiveness. YOLOv8 and Faster R-CNN architectures are then
applied to create pathology detection models in mammography images. The
work analyzed and classified identified breast pathologies at six levels,
taking into account different degrees of severity and characteristics of the
diseases. This approach allows for more accurate determination of disease
progression and provides additional data for more individualized treatment
planning. Classification results at various levels can improve the quality of
medical decisions and provide more accurate information to doctors, which
in turn improves the overall efficiency of diagnosis and treatment of breast
diseases. Experimental results demonstrate high accuracy and speed of
image processing, providing fast and reliable detection of potential breast
pathologies. The data obtained confirm the effectiveness of the use of
machine learning algorithms in the field of medical diagnostics, providing
prospects for the further development of automated systems for detecting
breast diseases in order to improve early diagnosis and treatment efficiency.
