Abstract
The World Health Organization (WHO) reports that breast cancer is one of the most frequent cancers among women, affecting almost 2.1 million women/year. As a consequence, fatality rate is high: 627,000 women died from breast cancer in 2018, which is approximately 15% [1] of all cancer deaths among women. Early detection (using AI-driven tools) can prevent from being worse. In this paper, we propose a Convolutional Neural Network (CNN) based approach for mammogram mass classification. The proposed method outperforms our existing feature-learning based model [2] on the same dataset. We experimentally achieved the best average recognition accuracy of 95.25% in separating malignant from benign masses. In our study on different age groups for mammogram mass classification, better results were observed from the age group: 61–75 years.
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Obaidullah, S.M., Mukherjee, H., Dhar, A., Goncalves, T., Santosh, K., Roy, K. (2022). Mammogram Mass Classification: A CNN-Based Technique Applied to Different Age Groups. In: Santosh, K., Hegadi, R., Pal, U. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2021. Communications in Computer and Information Science, vol 1576. Springer, Cham. https://doi.org/10.1007/978-3-031-07005-1_11
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