Optimizing PSO-CNN Parameters to Enhance Radiologist Accuracy in Breast Cancer Screening

Breast cancer screening is a critical area of medical diagnostics, where the accuracy and performance of radiologists play a pivotal role in early detection and diagnosis. In this Project, we present a novel approach aimed at enhancing radiologists' performance in breast cancer screening through the optimization of parameters for a PSO with CNN. We compare the results of our proposed method against an existing approach based on Deep Neural Networks (DNN) in terms of accuracy, specificity, and the types of cancer detected, including both benign and malignant cases. The existing method employs DNN as the primary algorithm, achieving an accuracy rate of 92.8%. While this performance is commendable, our proposed method, leveraging the power of PSO-CNN with optimized parameters, surpasses it with an accuracy rate of 95.5%. This improvement is of paramount significance in the context of breast cancer screening, where even small increments in accuracy can have substantial positive impacts on patient outcomes. Furthermore, when considering specificity, the existing DNN-based method achieves a specificity rate of 87.4%. In contrast, our proposed method utilizing PSO-CNN parameters achieves a specificity rate of 90%. This enhancement in specificity is vital, as it minimizes false positives, reducing patient anxiety and unnecessary follow-up procedures. Our proposed method based approach maintains the capability to identify both types of cancer, aligning with the existing DNN-based method in this regard. Finally the potential of utilizing proposed method parameters to enhance radiologists' performance in breast cancer screening.