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Improving mass discrimination in mammogram-CAD system using texture information and super-resolution reconstruction

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Abstract

Screening helps to reduce mortality in the breast cancers. Mammography is a screening procedure used to detect breast cancer at an early stage. Computer-aided detection (CAD) systems can help in mammograms examination. Automatic differentiation between benign and malignant mammographic masses is a challenging task, due to high variability in mass structures. That is why, CAD systems frequently misdiagnose breast cancer. This paper presents a new CAD approach for mass detection in digital mammograms. The purpose of the proposed approach is to improve the discrimination between benign mass and malignant mass by reinforcing their statistics texture features. To achieve this aim, a new step based on super-resolution reconstruction is added to multistage CAD system. The proposed approach gives very good results comparing to other recent works. It achieves 96.7% classification accuracy using the MIAS (Mammography Image Analysis Society) dataset. This work shows that a super-resolution based approach improves the performance of the evaluated texture methods and thus outperforms benign/malignant mass classification for digital mammograms.

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Authors and Affiliations

  1. Department of Computer Science, Laboratory of Research in Computer Science (LRI), University of Badji Mokhtar, P.O. Box 12, 23000, Annaba, Algeria

    Sawsen Boudraa, Ahlem Melouah & Hayet Farida Merouani

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  1. Sawsen Boudraa
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  2. Ahlem Melouah
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  3. Hayet Farida Merouani
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Correspondence to Sawsen Boudraa.

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Boudraa, S., Melouah, A. & Merouani, H.F. Improving mass discrimination in mammogram-CAD system using texture information and super-resolution reconstruction. Evolving Systems 11, 697–706 (2020). https://doi.org/10.1007/s12530-019-09322-4

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