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

ABSTRACT: 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

SUBMITTER: Jakkaladiki SP 

PROVIDER: S-EPMC12453785 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature