Breast Cancer Detection Using Hybrid Deep Belief Network& PSO

An important focus of medical research is breast cancer detection, with the goal of enhancing early diagnosis and treatment results. In order to improve breast cancer diagnosis, this study suggests a hybrid model that combines a Deep Belief Network (DBN) with Particle Swarm Optimization (PSO). From the breast cancer dataset, complicated characteristics are automatically learned and extracted using the DBN. It is made up of numerous layers of Restricted Boltzmann Machines (RBMs). The DBN’s hyperparameters are optimized using PSO to guarantee the model operates at peak efficiency. The model is trained and evaluated using the widely available Wisconsin Breast Cancer Dataset. Our results demonstrate the promise of the hybrid DBN-PSO model for reliable breast cancer diagnosis by demonstrating its superior accuracy and robustness over traditional machine learning techniques. This strategy makes use of PSO’s effective global search mechanism in addition to DBNs’ potent feature learning capabilities, offering medical professionals a potentially useful tool for early breast cancer identification.