An efficient deep convolutional network model using mask images for multiclass classification of breast cancer ultrasound images
Yazarlar (1)
Dr. Öğr. Üyesi Kadir Can BURÇAK Kırşehir Ahi Evran Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SCOPUS dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Neural Computing and Applications | ||
| Dergi ISSN | 0941-0643 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 09-2025 |
| Cilt / Sayı / Sayfa | 37 / 32 / 26983–27002 | DOI | 10.1007/s00521-025-11653-0 |
| Makale Linki | https://doi.org/10.1007/s00521-025-11653-0 | ||
| UAK Araştırma Alanları |
Görüntü İşleme
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| Özet |
| Breast cancer begins in the breast tissue when mutated cells grow out of control and eventually form a tumor. One of the most common causes of death among women worldwide is breast cancer. Early diagnosis and treatment can increase the likelihood of cancer prevention and recovery. Breast ultrasound analysis performed by medical professionals requires high competence in interpreting images, is time-consuming, and creates a negative situation in terms of the treatment process. Artificial intelligence methods have shown great success in the development of medical diagnosis and diagnostic models. When combined with artificial intelligence techniques, breast ultrasound images can produce good results in the detection and classification of breast cancer. This study focused on multiclass classification of breast cancer ultrasound images collected via ultrasound scanning via deep learning methods. In the first … |
| Anahtar Kelimeler |
| Breast cancer | Deep learning | Depthwise separable convolutions | Medical ultrasound image classification | Transfer learning |
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| Google Scholar | 2 |