چكيده به لاتين
Abstract:
Breast cancer is a kind of cancer that begins with breast tissue. Across the world, breast cancer is the most common type of cancer in women, accounting for 25% of all cases of cancer. Diagnosis and identification of breast cancer can be done by imaging methods such as diagnostic mammography, magnetic resonance imaging, ultrasound, and heat therapy. However, for tissue detection, tissue is the only safe way. The method involves the collection of cells or chest tissues that are stacked across the microscope slides for staining and microscopic examination. Diagnosis on histological images is the best way to detect various types of cancers, including breast cancer. These issues can be examined by an automated screening method that provides accurate and accurate methods. Given the human error in detecting cancer from histological images, this project attempted to provide an algorithm for the automatic and independent detection of human cancer cells. We used the BreaKHis dataset in this thesis, which has a large dataset compared with other data on breast cancer images. Due to the large number of data, the results obtained from this data set are more reliable. Due to the complexity of the tissues of the breast and thus the histological histogram of the breast, the extraction of the characteristics of these images is very important. We used eight different descriptor extractions and, given the complexity of histological images, we applied a variety of feature selection methods to improve the performance of the model on the features to provide the best possible features that maximized the resolution between nicer images and Get malignant. Then, four types of stratigraphy were used for classification. We obtained the matrix of mixing and ROC curve for evaluation of the model. Finally, AUC criteria, accuracy, specificity and sensitivity indicate the superiority and competitiveness of this algorithm in comparison with other algorithms used in this field.
Keywords: breast cancer, histological images and descriptor
