Leveraging Synthetic Mammograms to Enhance Deep-Learning Performance for Breast Cancer Classification Using EfficientNetV2L Architecture

Sutjiadi, Raymond and Sendari, Siti and Herwanto, Heru Wahyu and Kristian, Yosi (2025) Leveraging Synthetic Mammograms to Enhance Deep-Learning Performance for Breast Cancer Classification Using EfficientNetV2L Architecture. EAI Endorsed Transactions on AI and Robotics.

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Abstract

INTRODUCTION: To improve survival rates for breast cancer, a leading cause of female mortality globally, early detection is essential. This study presents a deep learning framework for classifying mammogram images as normal or abnormal.
OBJECTIVES: This research aims to enhance the performance of a

Item Type: Article
Date Deposited: 04 Mar 2026 20:15
Last Modified: 10 Apr 2026 18:14
URI: http://eprints.eai.eu/id/eprint/59921

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