BCDNN: Enhancing CNN Model for Automatic Detection of Breast Cancer Using Histopathology Images - Anumalla, Koushik; Kumar, G. Sunil ; Vani, M. Sree; Rao, Kuncham Sreenivasa (IAES Indonesian Section, 2025)
The United Nations has identified health and well-being for all as one of its sustainable development goals. Research efforts in the healthcare domain worldwide are aligned with this goal. According to the World Health Organization (WHO), there has been an increasing incidence of breast cancer globally. The emergence of Artificial Intelligence (AI) has enabled learning-based approaches for diagnosing various ailments in the healthcare domain. Numerous efforts have been designed to efficiently diagnose breast cancer using deep learning algorithms, with the Convolutional Neural Network (CNN) being the widely used model due to its efficiency in processing medical images. However, CNN-based models may experience deteriorated performance without empirical studies to improve the underlying architecture. Motivated by this fact, our paper proposes a deep learning-based system for breast cancer diagnostic automation by enhancing a CNN model called the Breast Cancer Detection Neural Network (BCDNN). We also introduce an algorithm called Enhanced Deep Learning for Breast Cancer Detection (EDL-BCD), which leverages the enhanced deep learning model for better disease diagnosis performance. Our evaluation with a benchmark dataset comprising breast histopathology images shows that our suggested framework significantly outperforms state-of-the-art models, achieving an impressive accuracy of 97.99%. Therefore, the proposed system can be integrated with healthcare applications to assist in automatic screening by utilizing histopathology pictures to visualize breast cancer.