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Breast cancer, a highly prevalent malignancy worldwide, re quires novel approaches for early detection and precise diagnosis. In 2020, the World Health Organization reported around 2.3 million new cases, highlighting an urgent necessity for enhanced screening methodologies and treatment strategies. The multifactorial characteristics of breast cancer, shaped by genetic predispositions, hormonal influences, age, lifestyle decisions, and environmental exposures, highlight the necessity of forum- lasting effective preventive interventions. This study investigates sophisticated diagnostic prediction methodologies employing cutting-edge ensemble deep learning methods, particularly Fully Connected Deep Neural Networks (DNNs) and Generative Adversarial Networks (GANs), to categorize cancers as malignant or benign. Our methodology employs an extensive machine learning pipeline, commencing with data acquisition from a pre-existing breast cancer dataset. The dataset includes essential tumor attributes, like radius and texture, facilitating accurate categorization. Utilizing DNNs, recognized for their efficacy in binary classification tasks, we evaluate model performance through metrics such as accuracy, precision, recall, and AUC. Generative Adversarial Networks (GANs) are employed to enhance the dataset, rectifying potential imbalances and strengthening the resilience of our model. The societal ramifications of this research are substantial, as early detection and customized treatment strategies enhance survival rates and quality of life for patients. The investigation of multi-omics data integration and the prospective use of telemedicine underscores new research directions that could enhance breast cancer detection and therapy. The emotional and economic burdens of breast cancer are substantial, highlighting the need for innovative and effective solutions in the continued fight against this illness. This project seeks to enhance diagnostic accuracy and outcomes for breast cancer patients through the utilization of modern machine learning techniques.

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Integrating DL in Breast Cancer Detection: A Study on DNN and GAN Models for Early Detection

  • 会议论文

  • 发表日期：

    2026年

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  • Karthik Oggu,
  • Yash Jain,
  • Sai Nagesh Vadlani,
  • Harish Reddy Thota

摘要

Breast cancer, a highly prevalent malignancy worldwide, re quires novel approaches for early detection and precise diagnosis. In 2020, the World Health Organization reported around 2.3 million new cases, highlighting an urgent necessity for enhanced screening methodologies and treatment strategies. The multifactorial characteristics of breast cancer, shaped by genetic predispositions, hormonal influences, age, lifestyle decisions, and environmental exposures, highlight the necessity of forum- lasting effective preventive interventions. This study investigates sophisticated diagnostic prediction methodologies employing cutting-edge ensemble deep learning methods, particularly Fully Connected Deep Neural Networks (DNNs) and Generative Adversarial Networks (GANs), to categorize cancers as malignant or benign. Our methodology employs an extensive machine learning pipeline, commencing with data acquisition from a pre-existing breast cancer dataset. The dataset includes essential tumor attributes, like radius and texture, facilitating accurate categorization. Utilizing DNNs, recognized for their efficacy in binary classification tasks, we evaluate model performance through metrics such as accuracy, precision, recall, and AUC. Generative Adversarial Networks (GANs) are employed to enhance the dataset, rectifying potential imbalances and strengthening the resilience of our model. The societal ramifications of this research are substantial, as early detection and customized treatment strategies enhance survival rates and quality of life for patients. The investigation of multi-omics data integration and the prospective use of telemedicine underscores new research directions that could enhance breast cancer detection and therapy. The emotional and economic burdens of breast cancer are substantial, highlighting the need for innovative and effective solutions in the continued fight against this illness. This project seeks to enhance diagnostic accuracy and outcomes for breast cancer patients through the utilization of modern machine learning techniques.