A Hybrid Deep Learning Approach with Progressive Cyclical CNN and Firebug Swarm Optimization for Breast Cancer Detection

The progressive cyclical CNN strategy incorporates cyclical re-adjusted learning rates and a progressive training schedule to hasten and strengthen the model's convergence. Firebug Swarm Optimization (FSO) is introduced to adjust the hyperparameters of the CNN topology, including the learning rate and regularisation parameters, thereby enhancing training and feature-fusion processes. The performance evaluation test results indicate that the proposed technique is better than the conventional techniques in all the accuracy, recall and precision measurements, thereby making it suitable for early detection of breast carcinoma. This approach holds great potential for enhancing detection systems in clinics, allowing earlier and more accurate detection of malignant lesions.