Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram

  • Mugahed A. Al-antari
  • Mohammed A. Al-masni
  • Tae-Seong KimEmail author
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1213)


For computer-aided diagnosis (CAD), detection, segmentation, and classification from medical imagery are three key components to efficiently assist physicians for accurate diagnosis. In this chapter, a completely integrated CAD system based on deep learning is presented to diagnose breast lesions from digital X-ray mammograms involving detection, segmentation, and classification. To automatically detect breast lesions from mammograms, a regional deep learning approach called You-Only-Look-Once (YOLO) is used. To segment breast lesions, full resolution convolutional network (FrCN), a novel segmentation model of deep network, is implemented and used. Finally, three conventional deep learning models including regular feedforward CNN, ResNet-50, and InceptionResNet-V2 are separately adopted and used to classify or recognize the detected and segmented breast lesion as either benign or malignant. To evaluate the integrated CAD system for detection, segmentation, and classification, the publicly available and annotated INbreast database is used over fivefold cross-validation tests. The evaluation results of the YOLO-based detection achieved detection accuracy of 97.27%, Matthews’s correlation coefficient (MCC) of 93.93%, and F1-score of 98.02%. Moreover, the results of the breast lesion segmentation via FrCN achieved an overall accuracy of 92.97%, MCC of 85.93%, Dice (F1-score) of 92.69%, and Jaccard similarity coefficient of 86.37%. The detected and segmented breast lesions are classified via CNN, ResNet-50, and InceptionResNet-V2 achieving an average overall accuracies of 88.74%, 92.56%, and 95.32%, respectively. The performance evaluation results through all stages of detection, segmentation, and classification show that the integrated CAD system outperforms the latest conventional deep learning methodologies. We conclude that our CAD system could be used to assist radiologists over all stages of detection, segmentation, and classification for diagnosis of breast lesions.


Medical image analysis Mammograms Breast lesion Computer-aided diagnosis (CAD) Deep learning Full resolution convolutional network (FrCN) Detection Segmentation Classification 



This work was supported by International Collaborative Research and Development Programme funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea) (N0002252). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (2019R1A2C1003713).


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mugahed A. Al-antari
    • 1
    • 2
  • Mohammed A. Al-masni
    • 1
  • Tae-Seong Kim
    • 1
    Email author
  1. 1.Department of Biomedical Engineering, College of Electronics and InformationKyung Hee UniversityYonginRepublic of Korea
  2. 2.Department of Biomedical EngineeringSana’a Community CollegeSana’aRepublic of Yemen

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