Breast cancer is the most common cancer in women. The need to diagnose this disease in the early stages increases the chance of treatment. Individuals and reduction of mortality with artificial intelligence approach in medicine. In implementing this applied and supervised study, a histopathological microscopic two data set, including respectively 124 and 576 patients with invasive breast cancer was used. Data preprocessing and image quality improvement, then image segmentation with U-Net network to separate cancer cells from healthy breast tissue and remove pert data, then by combining deep neural networks to extract effective features and by method The majority of data is based on the classification and screening system for the diagnosis of invasive breast cancer carcinoma. Performance in diagnosis and classification Breast cancer is one of the features of this study compared to other studies. According to the results obtained, this study is a step towards helping physicians and specialists in increasing the accuracy and sensitivity of breast cancer screening at the most optimal time, to the lesions. Triad the high risk to appropriate secondary care and increase patients' chances of survival with timely treatment.
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asadzadeh, S., & Ravaei, B. (2023). diagnosis of breast cancer at the molecular - cellular level with an artificial intelligence approach. Journal of Modeling in Engineering, 21(72), 19-30. doi: 10.22075/jme.2022.26164.2261
MLA
samira asadzadeh; Bahman Ravaei. "diagnosis of breast cancer at the molecular - cellular level with an artificial intelligence approach", Journal of Modeling in Engineering, 21, 72, 2023, 19-30. doi: 10.22075/jme.2022.26164.2261
HARVARD
asadzadeh, S., Ravaei, B. (2023). 'diagnosis of breast cancer at the molecular - cellular level with an artificial intelligence approach', Journal of Modeling in Engineering, 21(72), pp. 19-30. doi: 10.22075/jme.2022.26164.2261
VANCOUVER
asadzadeh, S., Ravaei, B. diagnosis of breast cancer at the molecular - cellular level with an artificial intelligence approach. Journal of Modeling in Engineering, 2023; 21(72): 19-30. doi: 10.22075/jme.2022.26164.2261
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