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Automated early breast cancer detection and classification system

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Abstract

Early detection of breast cancer is clinically important to reduce the mortality rate. In this study, a new computer-aided detection (CAD) and classification system is introduced to classify two types of mammogram tumors (i.e., mass and calcification) as either benign or malignant. In this CAD system, the tumor-like regions (TLRs) are identified using the automated optimal Otsu thresholding method. Afterward, deep convolutional neural networks (CNNs) process the extracted TLRs to extract relevant mammogram features, investigating AlexNet and ResNet-50 architectures. The normalized extracted CNN features are further input to a support vector machine classifier to decode the classes of mammogram structures (i.e., Benign Calcification, Benign Mass, Malignant Calcification, and Malignant Mass nodules). The experimental results are tested on 2800 mammogram images from the Curated Breast Imaging Subset of Digital Database of Screening Mammography, a publicly available dataset. The accuracy of the proposed CAD system, to classify the ROI into one of the four classes, achieves 0.91 using AlexNet and 0.84 using ResNet-50 models, using fivefold cross-validation. Comparison results with the related methods confirm the advantages of the proposed CAD system.

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Authors and Affiliations

  1. Electronics and Communication Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt

    Asmaa A. Hekal, Ahmed Elnakib & Hossam El-Din Moustafa

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  1. Asmaa A. Hekal
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  2. Ahmed Elnakib
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  3. Hossam El-Din Moustafa
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Correspondence to Ahmed Elnakib.

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Hekal, A.A., Elnakib, A. & Moustafa, H.ED. Automated early breast cancer detection and classification system. SIViP 15, 1497–1505 (2021). https://doi.org/10.1007/s11760-021-01882-w

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