An Experimental and Clinical Study on Advanced Breast Cancer Detection Using Convolutional Neural Networks with Improved Optimization Techniques

One of the most common and fatal cancers impacting women globally is breast cancer. For the best chance of survival, breast cancer must be found early. In order to help detect cancer, Convolutional Neural Networks (CNNs) have been effectively used in medical picture analysis. However, the presence of noise, artefacts, and other variables in medical pictures frequently affects how well CNNs work. It has been discovered that the performance of CNNs in a variety of applications may be enhanced by the use of sophisticated optimisation techniques. The aim of this study is to investigate the effectiveness of applying improved optimization techniques, namely, the Monarch Butterfly Optimization (MBO) algorithm, in conjunction with CNNs for advanced breast cancer detection. Our study compares the performance of CNNs with traditional statistical methods, such as receiver operating characteristic analysis on breast cancer imaging datasets. We also evaluate the clinical usefulness of CNNs in breast cancer detection in clinical settings. Our experimental results indicate that CNNs with improved optimization techniques achieve high accuracy in breast cancer detection and have the potential to improve the reliability of conventional diagnostic methods. Furthermore, the clinical findings demonstrated the feasibility of using advanced CNN models, combined with novel optimization techniques, for early breast cancer detection. The findings of this study will advance the field of medical imaging and contribute to better cancer management and treatment options for women.