ACNGCNN: Improving Efficiency of Breast Cancer Detection and Progression using Adversarial Capsule Network with Graph Convolutional Neural Networks

New diagnostic methods are needed to improve the accuracy and efficiency of breast cancer detection and progression. Although successful, current methods frequently lack precision, accuracy, and timeliness, especially in the early phases Of breast cancer progression. Our research proposes a new model using deep learning to improve breast cancer detection and classification, addressing constraints. Our breast cancer image and sample preprocessing approach combines a non-local means filter (NLM) and Generative Adversarial Networks (GAN). The model classifies datasets using LSTM with BiGRU-based Recurrent ShuffleNet V2, a highly efficient and accurate technique for sequential data samples. The integration of a Capsule Network with Graph Convolutional Neural Networks (CNGCNN) significantly improves breast cancer detection. This method was carefully tested on BreaKHis. The results were amazing, showing gains across multiple metrics: 4.9% greater precision, 3.5% higher accuracy, 3.4% higher recall, 2.5% higher AUC (Area Under the Curve), 1.9% higher specificity, and 3.4%decreased delay in the identification of breast cancer stages. Particularly striking was the model’s performance in diagnosing illness development, where it displayed 3.5% greater precision, 3.9% higher accuracy, 4.5% higher recall, 3.4% higher AUC, 2.9% higher specificity, and 1.5% lower latency. Significant clinical impacts result from this work. Our methodology enables early diagnosis and precise staging of breast cancer, enabling focused therapies to improve patient outcomes and survival rates. The greater precision and reduced time lag in diagnosing disease progression also allow for more effective monitoring and treatment modifications. Overall, this study marks a considerable improvement in the field of breast cancer diagnostics, delivering a more efficient, accurate, and reliable tool for healthcare providers in their fight against this ubiquitous disease.

@article{Pallapu2024,
  title     = {ACNGCNN: Improving Efficiency of Breast Cancer Detection and Progression using Adversarial Capsule Network with Graph Convolutional Neural Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {5},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Srinivasa Rao Pallapu and Khasim Syed},
  doi       = {10.14569/IJACSA.2024.01505142},
  url       = {https://doi.org/10.14569/IJACSA.2024.01505142}
}