Systematical Analysis and Pathological Classification of Breast Cancer from Mammographic Images with Using Specific Machine Learning Methods
Yazarlar (1)
Doç. Dr. Ali Berkan URAL Kafkas Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
|
||
| Dergi Adı | Traitement Du Signal (Q3) | ||
| Dergi ISSN | 0765-0019 Dergi Bilgileri (2022) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 10-2022 |
| Cilt / Sayı / Sayfa | 39 / 6 / 2149–2156 | DOI | 10.18280/ts.390628 |
| Makale Linki | https://www.iieta.org/Journals/TS/Current | ||
| UAK Araştırma Alanları |
Yapay Zeka
|
||
| Özet |
| For years, breast cancer has been a serious problem and malignant tumor case primarily causes death of women all around the world. In this paper, a computer based breast tumor analysis and pathological case classification system has been achieved and some novelties are included to the image processing methods, especially in segmentation and base frequency distribution acquisition of the processed image and classification part. First, the possible noises and artifacts are eliminated by using common filtering. Second, the filtered images are segmented with integrating gray level Image Processing methods. Then, these images (ROIs) are converted to the base frequency distribution images with using Fast Fourier Transform (FFT) and Lab&HSV color spaces. The most important key for these images is frequency distribution can be obtained with specific color tones and totally 100 images (50 benign-50 … |
| Anahtar Kelimeler |
| benign tumor | breast cancer | feature extraction | image classification | machine learning | malign tumor |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 1 |
| Scopus | 1 |
| Google Scholar | 2 |