Ebrahim Mohammed Senan1 , Fawaz Waselallah Alsaade2 , Mohammed Ibrahim Ahmed Al-mashhadani3 , Theyazn H.H aldhyani This email address is being protected from spambots. You need JavaScript enabled to view it.4, and Mosleh Hmoud Al-Adhaileh5

1Department of Computer Science & Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India
2College of computer sciences and information technology, King Faisal University
3AL-Iraqia University-College of Education- Computer Department, Baghdad-Iraq
4Community College of Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi
5Deanship of E-learning and distance education King Faial University Saudi Arabia


Received: November 22, 2020
Accepted: December 11, 2020
Publication Date: June 1, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202106_24(3).0007  


Breast cancer is one of the most common and deadly types of cancer that develops in the breast tissue of women worldwide. This is why researchers and experts are interested in developing a computer-aided diagnostic system (CAD) for diagnosing histopathological images of breast cancer. CAD has contributed to increasing the diagnostic accuracy of the biopsy tissue using eosin stained and hematoxylin images. Most CAD systems have used traditional methods to extract handcrafted features, which are imprecise in diagnosis and time-consuming. The diagnostics by both CAD and the calculations are used to reduce the pathologist’s workload and improve accuracy. In this study, the proposed convolutional neural network (AlexNet) approach to extract the deepest features from the BreaKHis dataset to diagnose breast cancer as either benign or malignant. In the current proposal, the study performed four experiments according to a magnification factor (40X, 100X, 200X and 400X). Each experiment contains 1407 images. The network was trained and validated on 80 % tissue images and 20 % for testing. The proposed system obtained accuracy, sensitivity, specificity, and AUC, 95 %, 97 %, 90 % and 99.36 % respectively.

Keywords: convolutional neural network, Breast cancer, BreakHis Dataset, Transfer learning


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