Advanced CNN-Based Classification and Segmentation for Enhanced Breast Cancer Ultrasound Imaging
Yazarlar (2)
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
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| Makale Alt Türü | Ulusal alan endekslerinde (TR Dizin, ULAKBİM) yayınlanan tam makale |
| Dergi Adı | Gazi University Journal of Science Part A: Engineering and Innovation |
| Dergi ISSN | 2147-9542 |
| Dergi Tarandığı Indeksler | TR DİZİN |
| Makale Dili | İngilizce |
| Basım Tarihi | 12-2024 |
| Cilt No | 11 |
| Sayı | 4 |
| Sayfalar | 647 / 667 |
| DOI Numarası | 10.54287/gujsa.1529857 |
| Makale Linki | https://doi.org/10.54287/gujsa.1529857 |
| Özet |
| Breast cancer (BC) is one of the primary causes of mortality in women globally. Thus, early and exact identification is critical for effective treatment. This work investigates deep learning, more especially convolutional neural networks (CNNs), to classify BC from ultrasound images. We worked with a collection of breast ultrasound images from 600 patients. Our approach included extensive image preprocessing techniques, such as enhancement and overlay methods, before training various deep learning models with particular reference to VGG16, VGG19, ResNet50, DenseNet121, EfficientNetB0, and custom CNNs. Our proposed model achieved a remarkable classification accuracy of 97%, significantly outperforming established models like EfficientNetB0, MobileNet, and Inceptionv3. This research demonstrates the ability of advanced CNNs, when paired with good preprocessing, to significantly enhance BC classification from ultrasound images. We further used Grad-CAM to make the model interpretable so we may see which parts of the images the CNNs focus on when making decisions. |
| Anahtar Kelimeler |
| CNN | Classification | Deep Learning | Breast Cancer | Image Processing |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| TRDizin | 2 |
| Google Scholar | 3 |