Deep Learning in Breast Cancer Diagnosis and Treatment Using Convolutional Neural Networks and Brain Storm Optimization

Breast cancer remains one of the leading causes of mortality among women worldwide, underscoring the urgent need for early and accurate diagnosis. While Convolutional Neural Networks (CNNs) have been extensively used in medical image analysis for breast cancer detection, their performance is highly sensitive to network architecture and hyper parameter selection. This paper introduces a novel hybrid framework that integrates Brain Storm Optimization (BSO) with CNNs to enhance diagnostic accuracy and optimize treatment planning. By employing BSO—a population-based optimization algorithm inspired by human brainstorming—we automatically tune CNN parameters.