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.
Łukasiewicz, M. Czeczelewski, A. Forma, ans et al., "Breast Cancer Epidemiology, Risk Factors, Classification, Prognostic Markers, and Current Treatment Strategies an Updated Review", Cancers, Vol.13, NO.4287, August 2021, pp. 42-87. DOI:10. 3390/cancers13174287.
Karidio and S. Sanlier, "Reviewing cancer’s biology: an eclectic approach", J Egypt Natl Canc Inst Vol.33, NO.32, November 2021, pp. 1-17. DOI: 10. 1186/s43046-021-00088-y.
Baghban, L. Roshangar, R. Jahanban Esfahlan and et al., "Tumor microen vironment complexity and therapeutic implications at a glance", Cell Commun Signal Vol.18, NO.59. April 2020, pp.1-19. DOI: 10. 1186/s12964-020-0530-4.
M Jafari and M. Hasanzadeh, "Non-invasive bioassay of Cytokeratin Fragment 21. 1 (Cyfra 21. 1) protein in human saliva samples using immunoreaction method: An efficient platform for early-stage diagnosis of oral cancer based on biomedicine", Biomedicine and Pharmacotherapy, Vol 131. NO. 110671, November 2020, pp.1-14.
L. Niedzwiedz, L. Knifton, K.A. Robb, and et al., "Depression and anxiety among people living with and beyond cancer: a growing linical and research priority", BMC Cancer Vol.19, NO.943, October 2019, pp. 1-8. DOI: 10. 1186/s12885-019-6181-4.
Sechopoulos, J. Teuwen and R. Mann, "Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: State of the art, Seminars in Cancer Biology" Vol. 72. July 2021, pp.214-225. DOI: 10.1016/j. semcancer. 2020.06. 002.
M. McKinney, M. Sieniek and et al., "International evaluation of an AI system for breast cancer screening", Nature, January 2020, pp. 89-94. DOI: 10.1038/s41586-019-1799-6.
H. Lin and B.K. Kujabi, "Chuang C-L, Lin C-S, Chiu C-J. Application of Deep Learning to Construct Breast Cancer Diagnosis Model", Applied Sciences, Vol.12(4), NO.1957, February 2022, pp.19-57. DOI:10. 3390/app12041957
A. Alanazi, M.M. Kamruzzaman and et al., "Boosting Breast Cancer Detection Using Convolutional Neural Network", Journal of Healthcare Engineering, Vol.2021, April 2021, pp. 1-11. DOI: 10. 1155/2021/5528622.
امین رضایی پناه، علی مبارکی و سعید بحرانی خادمی، "بهینه سازی شبکه عصبی MLPبا استفاده از الگوریتم ژنتیک موازی FinGrain برای تشخیص سرطان سینه"، نشریه مدلسازی در مهندسی، دوره 17 ، شماره 57، تیر 1398، صفحه 173-186.
اصغر زارع و علی محمدزاده، "حذف نویز ضربه‌ای از تصاویر دیجیتالی مبتنی بر تخمین توزیع مکانی نویزها"، نشریه مدلسازی در مهندسی، دوره 12، شماره39 ، زمستان 1393، صفحه 1-17.
پانیذ تیموری، مهدی مزینانی و راحیل حسینی، "ارایه یک مدل هوشمند قطعه‌بندی مبتنی بر منطق فازی و تبدیل موجک گسسته در تصاویر دیجیتالی جهت شناسایی سرطان معده" نشریه مدل سازی در مهندسی، دوره 18 ، شماره 63، بهمن 1399، صفحه 131-150.
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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