Detection of breast cancer via deep convolution neural networks using MRI images

  • Ahmet Haşim Yurttakal
  • Hasan ErbayEmail author
  • Türkan İkizceli
  • Seyhan Karaçavuş


Breast cancer is the type of cancer that develops from cells in the breast tissue. It is the leading cancer in women. Early detection of the breast cancer tumor is crucial in the treatment process. Mammography is a valuable tool for identifying breast cancer in the early phase before physical symptoms develop. To reduce false-negative diagnosis in mammography, a biopsy is recommended for lesions with greater than a 2% chance of having suspected malignant tumors and, among them, less than 30 percent are found to have malignancy. To decrease unnecessary biopsies, recently, Magnetic Resonance Imaging (MRI) has also been used to diagnose breast cancer. MRI is the highly recommended test for detecting and monitoring breast cancer tumors and interpreting lesioned regions since it has an excellent capability for soft tissue imaging. However, it requires an experienced radiologist and time-consuming process. On the other hand, convolutional neural networks (CNNs) have demonstrated better performance in image classification compared to feature-based methods and show promising performance in medical imaging. Herein, CNN was employed to characterize lesions as malignant or benign tumors using MRI images. Using only pixel information, a multi-layer CNN architecture with online data augmentation was designed. Later, the CNN architecture was trained and tested. The accuracy of the network is 98.33% and the error rate 0.0167. The sensitivity of the network is 1.0 whereas specificity is 0.9688. The precision is 0.9655.


Breast cancer Convolutional neural network Classification 



This study was not supported by any funding source. The authors declare that they have no conflict of interest. The authors alone are responsible for the content and writing of the paper. All authors read and approved the final manuscript.

Also, we are thankful for the anonymous referees for their valuable comments to improve the quality of the article.

Authors Contribution

Ahmet Haşim Yurttakal and Hasan Erbay designed the model and the computational framework; analyzed the data. Türkan İkizceli and Seyhan Karaçavuş acquired the dataset and analyzed the data. All authors discussed the results and contributed to the final manuscript.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Technologies Department, Technical Sciences Vocational SchoolBozok UniversityYozgatTurkey
  2. 2.Computer Engineering Department, Engineering FacultyKırıkkale UniversityKırıkkaleTurkey
  3. 3.Haseki Training and Research Hospital, Department of RadiologyUniversity of Health SciencesİstanbulTurkey
  4. 4.Kayseri Training and Research Hospital, Department of Nuclear MedicineUniversity of Health SciencesKayseriTurkey

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