Spilled Deep Capsule Neural Network with Skill Optimization Algorithm for Breast Cancer Recognition in Mammograms

Breast cancer stands as the main reason for cancer deaths among women worldwide so early detection plays an essential role in raising both survival statistics and treatment effectiveness. The common breast cancer screening method known as mammography leads radiologists to struggle when evaluating mammograms because they often make incorrect diagnoses that result in delayed medical procedures. Standard assessment techniques experience difficulties in identifying faint abnormalities which causes both incorrect positive and negative results. The proposed research implements a new methodology that utilizes MIAS dataset to detect breast cancer. The Adaptive Morphological Wavelet Perona-Malik Filter Algorithm operates on mammogram images for preprocessing to optimize quality and save vital image characteristics. A Spilled Deep Capsule Neural Network function (SDCN) utilizes the network for effective mammographic image detection and feature extraction. The Skill Optimization Algorithm (SOA) serves as a parameter optimization tool which boosts both accuracy and efficiency of the model. The developed method succeeded in reaching 99.9% accuracy while surpassing previous standard examination methods. The research demonstrates how advanced deep learning and optimization algorithms support radiologists by detecting breast cancer properly and expeditiously which creates better patient healthcare outcomes.