Breast Cancer Detection Based on Hybrid Deep Learning Models | ||||
| IJCI. International Journal of Computers and Information | ||||
| Article 17, Volume 10, Issue 3, November 2023, Page 119-125 PDF (607.23 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijci.2023.236078.1130 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Ibrahim Sayed Elaraby Mohamed1; Sameh Zarif  2; Hatem Abdel-Kader3; Mohamed Elsayed4
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| 1Department of Information Systems Management Higher Institute for qualitative studies Cairo, Egypt | ||||
| 2Faculty of computers and information, Menofia university | ||||
| 3Information SystemsDepartment Faculty of Computers and Information Menoufia University, Egypt | ||||
| 4Department of Diagnostic and Interventional Imaging Liver Institute Shebin Elkom, Egypt | ||||
| Abstract | ||||
| Early breast cancer diagnosis and detection are very important. It may greatly enhance treatment results and save a life. The absence of early cancer signs makes early identification challenging. Cancer continues to be one of the health subjects that many researchers work to advance. This study proposed a new hybrid model for classifying breast cancer images. The proposed framework consists of preprocessing stage and the proposed two models stage. For the preprocessing, we downsized every image from its original 50x50 to 32x32 pixel size, rotating and flipping all positive images for the Histology Images. The proposed hybrid model consists of a CNN model created from scratch and transfer learning based on EfficientNetB0 (CNN+ EfficientNetB0) to classify Invasive ductal carcinoma (IDC) into benign and malignant. According to tests, the CNN + EfficientNetB0 model has the highest accuracy compared to the other deep learning models. This model achieves 96% accuracy, 95% precision, 82.5% recall, and 88.3% F1- score. | ||||
| Keywords | ||||
| Invasive Ductal Carcinoma; Breast Cancer; Deep learning; Data Processing; Pre-trained Model | ||||
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