A hybrid deep learning approach with progressive cyclical CNN and firebug swarm optimization for breast cancer detection.

The practice of diagnosing breast cancer retains its scope for improvement in medical imaging, where every correct and timely diagnosis enhances the survival rate of patients. This article presents an integrated approach utilizing patch-wise breast image segmentation, hybrid deep feature extraction, followed by progressive cyclical convolutional neural networks (P-CycCNN), and firebug swarm optimization (FSO) to enhance breast cancer detection. This method first incorporates image segmentation by patches to break down the mammography images into smaller patches, which are easier to focus on and allow for the extraction of more features to boost detection rates. Hybrid feature extraction combines convolutional neural network (CNN) features extracted from pre-trained models with handcrafted features that describe texture and shape, thereby enabling the model to grasp the nuances of both coarse and fine images comprehensively. The progressive cyclical CNN strategy incorporates cyclical, re-adjusted learning rates and a progressive training schedule to accelerate and enhance the model's convergence. 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. Evaluated on the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) dataset, the proposed model achieved 98% test accuracy, 95% precision, 97.2% recall, 96% F1-score, and an AUC of 0.95, outperforming baseline CNN models by 4%-6% across key metrics. This approach holds great potential for enhancing detection systems in clinics, allowing earlier and more accurate detection of malignant lesions.