Exploiting the Performance of Marine Predators Optimization Algorithm in Combination with Neural Network Classifiers for Breast Mass Classification
International journal of intelligent engineering and systems · 2022
Recently, breast cancer has achieved top position in terms of cause of mortality among women of all age groups by surpassing the lung cancer.To improve the survival rate, timely assessments are essential.Mammography is the best modality among the most widely used timely detection modalities.The radiologists' manual reading may have an impact on the accuracy of the diagnosis.As a result, the computer-aided diagnosis (CAD) systems are being developed as tools to reduce the false alarms and to increase the diagnosis accuracy.In this study, an attempt has been made to improve the diagnosis performance of the CAD systems by incorporating recently developed marine predators algorithm (MPA) in conjunction with three different neural network classifiers including feedforward neural network (FFNN), cascade forward neural network (CFNN), and recurrent neural network (RNN).Unlike other existing studies, fully digital mammogram images from INbreast dataset have been employed for testing of the system proposed in this study.Experimental results reveal that the best classification performance [Accuracy 97.34%, Sensitivity: 98.40%, Specificity: 100.00%] is obtained when MPA is used in conjunction with RNN classifier.To demonstrate the usefulness of the proposed system, the obtained results are compared with the results obtained using already invented CAD systems in previously published studies using the same dataset.The findings suggest that the proposed system is acceptable for real-time clinical applications.