Computer Image Analysis System for Breast Cancer Diagnosis
Abstract
The article presents a system for computer analysis of medical images, designed to support a clinical decision-making system focused on diagnosing and treating breast cancer. This system assists physicians in accurately and efficiently assessing patient conditions, providing additional data to refine treatment strategies. Modern machine learning and deep learning algorithms enable the automatic detection and classification of tumor formations and the analysis of pathological changes in medical images, such as mammograms and MRIs.
The system operates through several stages, including image preprocessing, feature extraction, training on annotated data, and subsequent classification of identified structures. The methods used can identify even minimal deviations from the norm, which is crucial for early pathology detection. This tool becomes an essential asset in clinical practice, minimizing error risks and reducing specialists’ workload. The automated analysis capabilities aid doctors in making well-informed decisions, enhancing diagnostic efficiency and improving patient prognosis.
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