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Breast cancer is the most common cancer among women. Histological images are widely used for breast cancer diagnosing. In this research study, the fuzzy system of breast precancerous and cancerous conditions diagnosing is proposed. This system is based on the expert evaluation of histological images and can be used in medical practice.
In this paper, the authors investigated the main types of mammary dysplasia. In order to classify biomedical images, the researchers developed a basic model of convolutional neural network (CNN). Input parameters of the neural network to classify cytological and histological images were thoroughly researched and selected.
Breast cancer is the most common cancer among women. Cytological images are used to diagnose the breast cancer. In this thesis the fuzzy system of diagnosing of precancerous and cancerous conditions of the breast is proposed. This system is based on the expert evaluation of cytological images and can be used in medical practice.
Cytological (CI) and histological images (HI) of separate cells and cells groups are used to diagnose breast cancer. We propose an approach that combines computer vision algorithms and intelligent processing of images (fuzzy knowledge base). We also have researched several types of breast cancer and developed a database of CI and HI. On the base of a database an intellectual system of breast cancer...
We propose an intelligent system for processing histological and cytological images for diagnosing breast cancers. The main components of this system are based on a fuzzy-knowledge base and databases. The fuzzy-knowledge base contains rules for diagnosis, obtained from expert facts. The databases contain images and their quantitative characteristics. The software is implemented as ImageJ plugin.
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