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Value of Original and Generated Ultrasound Data Towards Training Robust Classifiers for Breast Cancer Identification

Author

Listed:
  • Bianca-Ştefania Munteanu

    (University Babes-Bolyai)

  • Alexandra Murariu

    (University Babes-Bolyai)

  • Mǎrioara Nichitean

    (University Babes-Bolyai)

  • Luminiţa-Gabriela Pitac

    (University Babes-Bolyai)

  • Laura Dioşan

    (University Babes-Bolyai)

Abstract

Breast cancer represents one of the leading causes of death among women, with 1 in 39 (around 2.5%) of them losing their lives annually, at the global level. According to the American Cancer Society, it is the second most lethal type of cancer in females, preceded only by lung cancer. Early diagnosis is crucial in increasing the chances of survival. In recent years, the incidence rate has increased by 0.5% per year, with 1 in 8 women at increased risk of developing a tumor during their life. Despite technological advances, there are still difficulties in identifying, characterizing, and accurately monitoring malignant tumors. The main focus of this article is on the computerized diagnosis of breast cancer. The main objective is to solve this problem using intelligent algorithms, that are built with artificial neural networks and involve 3 important steps: augmentation, segmentation, and classification. The experiment was made using a publicly available dataset that contains medical ultrasound images, collected from approximately 600 female patients (it is considered a benchmark). The results of the experiment are close to the goal set by our team. The final accuracy obtained is 86%.

Suggested Citation

  • Bianca-Ştefania Munteanu & Alexandra Murariu & Mǎrioara Nichitean & Luminiţa-Gabriela Pitac & Laura Dioşan, 2025. "Value of Original and Generated Ultrasound Data Towards Training Robust Classifiers for Breast Cancer Identification," Information Systems Frontiers, Springer, vol. 27(1), pages 75-96, February.
    • Handle: RePEc:spr:infosf:v:27:y:2025:i:1:d:10.1007_s10796-024-10499-6
    DOI: 10.1007/s10796-024-10499-6
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