Automated early breast cancer detection and classification system

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

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1497-1505
Number of pages9
JournalSignal, Image and Video Processing
Volume15
Issue number7
DOIs
StatePublished - Oct 2021

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

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