A Novel Transfer-Learning Model for Automatic Detection and Classification of Breast Cancer Based Deep CNN
Saber, A., Sakr, M., Abou-Seida, O., Keshk, A. (2021). A Novel Transfer-Learning Model for Automatic Detection and Classification of Breast Cancer Based Deep CNN. EKB Journal Management System, 2(1), 1-9. doi: 10.21608/kjis.2021.192207
Abeer Saber; Mohamed Sakr; Osama Abou-Seida; Arabi Keshk. "A Novel Transfer-Learning Model for Automatic Detection and Classification of Breast Cancer Based Deep CNN". EKB Journal Management System, 2, 1, 2021, 1-9. doi: 10.21608/kjis.2021.192207
Saber, A., Sakr, M., Abou-Seida, O., Keshk, A. (2021). 'A Novel Transfer-Learning Model for Automatic Detection and Classification of Breast Cancer Based Deep CNN', EKB Journal Management System, 2(1), pp. 1-9. doi: 10.21608/kjis.2021.192207
Saber, A., Sakr, M., Abou-Seida, O., Keshk, A. A Novel Transfer-Learning Model for Automatic Detection and Classification of Breast Cancer Based Deep CNN. EKB Journal Management System, 2021; 2(1): 1-9. doi: 10.21608/kjis.2021.192207

A Novel Transfer-Learning Model for Automatic Detection and Classification of Breast Cancer Based Deep CNN

Kafrelsheikh Journal of Information Sciences
Article 8, Volume 2, Issue 1, August 2021, Page 1-9  XML PDF (752.93 K)
Document Type: Original Article
DOI: 10.21608/kjis.2021.192207
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Authors
Abeer Saber email 1; Mohamed Sakr2; Osama Abou-Seida3; Arabi Keshk4
1Department of computer science, faculty of computers and information, Kafrelsheikh University
2Department of computer science, Faculty of computers and information, Menoufia University
3Computer Science Department, Faculty of Computers and Information Sciences, Kafrelsheikh University, Egypt.
4Faculty of Computers and Information, Menofia University, Egypt
Abstract
Breast cancer (BC) is a leading cause of cancer death among women in which breast cells develop out of control is by encouraging patients to receive timely care, early detection of BC increases the likelihood of survival. In this context, a new deep learning (DL) model is presented for automatic detection and classification of the suspected area of the breast based on the transfer learning (TL) technique. A pre-trained visual geometry group (VGG)-19, VGG16, and InceptionV3 networks are used in the presented model to transfer their learning parameters for improving the performance of breast tumor classification. The main goals of this project are to use segmentation to automatically determine the affected breast tumor region, reduce training time, and improve classification performance. In the presented model, the Mammographic Image Analysis Society (MIAS) dataset is used for extracting the breast tumor features. We have chosen four evaluation metrics for evaluating the performance of the presented model accuracy, sensitivity, specificity, and area under the ROC curve (AUC). The experiments showed that transferring parameters from the model of VGG16 is a powerful for BC classification than VGG19 and Inception V3 with overall specificity, accuracy, sensitivity, and AUC 98%,96.8%, 96%, and 0.99, respectively.
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