Hassan, E., Behery, G., farouk, R. (2024). Deep Learning Implementation in the Classification of Breast Medical Images. Bulletin of Faculty of Science, Zagazig University, 2023(4), 109-117. doi: 10.21608/bfszu.2023.199335.1262
Elham Ahmed Hassan; Gamal Behery; Roshdy farouk. "Deep Learning Implementation in the Classification of Breast Medical Images". Bulletin of Faculty of Science, Zagazig University, 2023, 4, 2024, 109-117. doi: 10.21608/bfszu.2023.199335.1262
Hassan, E., Behery, G., farouk, R. (2024). 'Deep Learning Implementation in the Classification of Breast Medical Images', Bulletin of Faculty of Science, Zagazig University, 2023(4), pp. 109-117. doi: 10.21608/bfszu.2023.199335.1262
Hassan, E., Behery, G., farouk, R. Deep Learning Implementation in the Classification of Breast Medical Images. Bulletin of Faculty of Science, Zagazig University, 2024; 2023(4): 109-117. doi: 10.21608/bfszu.2023.199335.1262
Deep Learning Implementation in the Classification of Breast Medical Images
1Mathematics Department ,Faculty of Science,Zagazig University, Sharqie, Egypt
2Computer Information Faculty of Computer Information System Damietta University, Egypt
3Department of Mathematics, Faculty of science, Zagazig university, Egypt
Abstract
Breast cancer is one of the prime purposes of ending women's life. For this purpose, mammogram analysis is an active manner that helps radiologists in the detection of breast cancer early. This paper uses deep learning models to classify mammographic images. The support vector machine (SVM) with deep learning features of a mammogram helps to classify breast tissue based on image processing techniques. Based on the values of these features of a digital mammogram, both deep learning models and SVM try to classify the breast tissue into basic categories normal, and abnormal given in the database (mini-MIAS database). Data augmentation mechanisms have been applied to increase the training set size to avoid overfitting. After making a comparison of some models, it became clear that the best result of the classification is 97.77 % by using the VGG model. These results will be useful in making medical classification images more accurate. By this method, a radiologist can detect if the breast has cancer or not.
View on SCiNiTO
Top