Optimizing breast cancer classification based on cat swarm-enhanced ensemble neural network approach for improved diagnosis and treatment decisions.

Breast cancer remains a formidable global health challenge, emphasizing the critical importance of accurate and early diagnosis for improved patient outcomes. In recent years, machine learning, particularly deep learning, has shown substantial promise in assisting medical practitioners with breast cancer classification tasks. However, achieving consistently high accuracy and robustness in the classification process remains a significant challenge due to the inherent complexity and heterogeneity of breast cancer data. This study introduces an innovative approach to optimize breast cancer classification using the CS-EENN Model by harnessing the combined power of Cat Swarm Optimization (CSO) and an Enhanced Ensemble Neural Network approach. The ensemble approach capitalizes on the strengths of EfficientNetB0, ResNet50, and DenseNet121 architectures, known for their superior performance in computer vision tasks, to achieve a multifaceted understanding of breast cancer data. CSO employed to optimize the architecture and hyperparameters of these neural networks, enhancing their performance by facilitating convergence and preventing overfitting. Experimental evaluations conducted on the publicly available 'Breast Histopathology Images' dataset from Kaggle demonstrate the effectiveness of the proposed approach. The CS-EENN model achieved an impressive accuracy of 98.19%, significantly outperforming conventional methods. These advancements expected to have a direct and favourable impact on the accuracy of breast cancer detection and subsequent treatment decisions.