Abstract
Breast cancer has increased decidedly among women. But with early diagnosis, a positive response to treatment can be given. Researchers are conducting various studies in imaging methods to detect the disease early and accurately. In this study, 9 cancerous images taken from the TCİA image data bank were detected by K-mean clustering and the Otsu threshold method. Performance metrics were evaluated by comparing them with marked reference images (ground truth) by the radiologist. For the clustering process, TPR (True Positive Rate) 0.89, FPR (False Positive Rate) 0.14, similarity 0.67, accuracy 0.87, sensitivity 0.89, exact hit ratio 0.86, specificity 0.87, F Score 0.87 were found, respectively. For Otsu, TPR (True Positive Rate) 0.84, FPR (False Positive Rate) 0.12, similarity 0.73, accuracy 0.84, sensitivity 0.84, exact hit 0.86, specificity 0.87, F Score 0.84 were calculated. The aim of this study is to determine the tumor boundaries more accurately and to use them in imaging devices in the field of health with pixel-based segmentation. As a result, both methods were successful can be used in the field and close to each other.
Highlights
Breast cancer is the most common cancer in women worldwide
In this study, a fully automated computer-aided diagnosis (CAD) algorithm was designed for manually segmented breast cancer images
This study aimed to reduce a radiographic error in examinations of cancerous tissue in patients diagnosed with breast cancer, which can be better determined with software
Summary
Breast cancer is the most common cancer in women worldwide. According to the American Cancer Society (ACS) report, approximately 2.6 million women have been diagnosed with invasive breast cancer, and approximately 40.000 women have died in 2020 (Cancer Facts and Figures, 2020). 13% of cancers in Canada are breast cancer. As well as late diagnosis, obesity, early or late menopause, have never given birth, fibrocystic diseases, the presence of abnormal cells, and the possibility of receiving hormone therapy are important factors in the formation of breast cancer These lesions typically have a size in diameter due to their very small sizes, microcalcifications can be quite difficult to detect. Benign calcifications come in uniform sizes with round or large elliptical shapes, but non-uniform, small, polymorphic, and spreading calcifications with heterogeneous volume and morphology have a higher chance of becoming malignant (Tan et al, 2020) Some anatomical structures, such as fibrous strands, breast borders, or hypertrophic lobules, are similar to microcalcifications in the mammographic image.
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