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According to this study, The biggest cause of death is breast cancer for women in the contemporary era. With 14% of cases predicted to result in death in 2018, breast cancer is anticipated to have the second-highest estimated mortality toll among women among all cancer types. With a 29% incidence rate across all cancer types, this has consequently suggested a serious worldwide health concern. In addition, 2,46,660 women had a diagnosis of breast cancer in 2016. Early detection has a major impact on the possibilities for diagnosis and treatment of breast cancer, which helps explain the disease’s 97.5% five-year survival rate. Conversely, if the diagnosis is delayed and the disease has spread to other organs, the patient’s five-year survival percentage drops to just 20.4%. In 2020, there were 6,84,996 deaths worldwide due to breast cancer (WHO, 2021). Early identification of breast cancer remains a significant global issue. Deep learning algorithms are rapidly gaining popularity due to their remarkable image classification performance, particularly for imaging applications linked to breast histopathology diagnosis. CNN are among the most often used deep learning model for health picture detection and analysis among all deep learning subcategories. Nevertheless, CNN requires regular parameter modifications and has a significant implementation computing cost. To address this issue, a number of previously trained models are being constructed using the provided network design. This work derives a transfer learning model from the 16-layer complex model architecture.

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Evaluation on Obtaining Advanced Features of CNN’s Deep Learning VGG 16 Architecture for Breast Cancer Detection

  • 会议论文

  • 发表日期：

    2025年

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  • G. Divya,
  • Sudha Arvind,
  • B. Premalatha,
  • Y. Aruna Suhasini Devi,
  • M. Kumara Swamy,
  • Lal Bahadur Pandey

摘要

According to this study, The biggest cause of death is breast cancer for women in the contemporary era. With 14% of cases predicted to result in death in 2018, breast cancer is anticipated to have the second-highest estimated mortality toll among women among all cancer types. With a 29% incidence rate across all cancer types, this has consequently suggested a serious worldwide health concern. In addition, 2,46,660 women had a diagnosis of breast cancer in 2016. Early detection has a major impact on the possibilities for diagnosis and treatment of breast cancer, which helps explain the disease’s 97.5% five-year survival rate. Conversely, if the diagnosis is delayed and the disease has spread to other organs, the patient’s five-year survival percentage drops to just 20.4%. In 2020, there were 6,84,996 deaths worldwide due to breast cancer (WHO, 2021). Early identification of breast cancer remains a significant global issue. Deep learning algorithms are rapidly gaining popularity due to their remarkable image classification performance, particularly for imaging applications linked to breast histopathology diagnosis. CNN are among the most often used deep learning model for health picture detection and analysis among all deep learning subcategories. Nevertheless, CNN requires regular parameter modifications and has a significant implementation computing cost. To address this issue, a number of previously trained models are being constructed using the provided network design. This work derives a transfer learning model from the 16-layer complex model architecture.