Classification of breast cancer with deep learning from noisy images using wavelet transform
Yazarlar (4)
Dr. Öğr. Üyesi Enes CENGİZ Sinop Üniversitesi, Türkiye
Dr. Öğr. Üyesi Muhammed Mustafa Kelek Afyon Kocatepe Üniversitesi, Türkiye
Yüksel Oǧuz
Afyon Kocatepe Üniversitesi, Türkiye
Afyon Kocatepe Üniversitesi, Türkiye
Cemal Yllmaz
Mingachevir State University, Azerbaycan
Mingachevir State University, Azerbaycan
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Biomedizinische Technik (Q4) | ||
| Dergi ISSN | 0013-5585 Dergi Bilgileri (2022) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Ingilizce | Basım Tarihi | 03-2022 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 16-03-2022 |
| Cilt / Sayı / Sayfa | 67 / 2 / 143–150 | DOI | 10.1515/bmt-2021-0163 |
| Makale Linki | http://dx.doi.org/10.1515/bmt-2021-0163 | ||
| UAK Araştırma Alanları |
Yapay Zeka
|
||
| Özet |
| In this study, breast cancer classification as benign or malignant was made using images obtained by histopathological procedures, one of the medical imaging techniques. First of all, different noise types and several intensities were added to the images in the used data set. Then, the noise in images was removed by applying the Wavelet Transform (WT) process to noisy images. The performance rates in the denoising process were found out by evaluating Peak Signal to Noise Rate (PSNR) values of the images. The Gaussian noise type gave better results than other noise types considering PSNR values. The best PSNR values were carried out with the Gaussian noise type. After that, the denoised images were classified by Convolution Neural Network (CNN), one of the deep learning techniques. In this classification process, the proposed CNN model and the VggNet-16 model were used. According to the … |
| Anahtar Kelimeler |
| breast cancer | classification | CNN | deep learning | denoising | wavelet transform |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 16 |
| Scopus | 22 |
| Google Scholar | 26 |