Examine Breast Cancer images using convolutional networks models for analysis | ||||
| Bulletin of Faculty of Science, Zagazig University | ||||
| Article 5, Volume 2025, Issue 2, June 2025, Page 50-60 PDF (1.52 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/bfszu.2024.307330.1417 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
abdelmounem ali mohamad   1; R M farouk2; Mohamed Abd Elaziz3
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| 11Mathematics Department, Faculty of Science, Zagazig University, sharqia, Egypt | ||||
| 2Department of Mathematics, Faculty of science, Zagazig university, Egypt | ||||
| 3Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt | ||||
| Abstract | ||||
| Examination of Breast Cancer (BC) is critical for patient outcomes, and bio-imaging methods such as Ultrasound Breast Image scans are essential for clinical assessment. However, manually analyzing these images is time-consuming and requires expertise. To tackle this issue, we suggest the use of an image classification model. This paper investigates the use of deep learning models to classify mammographic images for breast cancer. The AlexNet and VGG 19 models were tested using the ultrasound dataset, with the goal of accurately categorizing images into benign and malignant classes. The results showed high performance for all models. This suggests that deep learning models, especially advanced ones like the proposed updated model, are effective in identifying mammographic images and could improve breast cancer accuracy. Our proposed method relies on using a Convolutional Neural Network (CNN) model for feature extraction. After extracting the features, we perform fine-tuning to enhance the model's performance. Finally, the extracted features are passed to a Support Vector Machine (SVM) for classification or examination tasks. Our method not only decreases the workload and analysis time for doctors but also improves the accuracy of tumor detection. Automating image analysis and identifying tumors early and accurately can result in improved patient care. Further research is required to improve these models and investigate their application in clinical environments. | ||||
| Keywords | ||||
| Image Classification; Convolutional Neural Network; Deep Learning; Models; Breast Cancer | ||||
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