ENHANCING BREAST CANCER DETECTION WITH CAPSULE NETWORKS: A DEEP LEARNING APPROACH
Keywords:
Breast Cancer Detection, Capsule Networks (CapsNets), Deep Learning, Machine Learning, Convolutional Neural Networks (CNNs), Precision Medicine, Medical Imaging, Classification Accuracy, Recall, F1 Score, Spatial Hierarchy, Dynamic Routing, Model Interpretability, Domain Adaptation, Transfer Learning, Data Standardization, Diagnostic Imaging, Supervised Learning, Feature Extraction, Kaggle DatasetAbstract
The potential of Capsule Networks (CapsNets) to improve breast cancer detection is investigated in this research. The paper examines the effectiveness of current advances in deep learning, namely CapsNets, and compares them to conventional machine learning models. CapsNets continuously show better performance in differentiating between benign and malignant cases by using extensive evaluation criteria like as accuracy, precision, recall, and F1 score. CapsNets’ ability to adjust to different criteria is further highlighted graphically, highlighting their potential to increase early detection rates. Although there have been tremendous advances with CapsNets, issues with model interpretability and computing complexity still exist. Additional study on domain adaptation strategies is also required due to generalization concerns to different demographics and imaging modalities. CapsNets have revolutionary promise in the diagnosis of breast cancer, notwithstanding these drawbacks. Subsequent investigations have to concentrate on tackling significant obstacles and broadening the suitability of CapsNets in clinical environments by employing group learning strategies and implementing data standardization programs. CapsNets provide a possible avenue to transform breast cancer detection through coordinated efforts, which will ultimately result in better patient outcomes and increased survival rates.
