Liver Tumor Detection using Artificial Neural Networks for Medical Images

The purpose of this study is to compare the performance of Back-Propagation Neural Network and Support Vector Machine (SVM) for liver cancer classification. The performance of both models is compared and validated in terms of accuracy within the true positive rate and false positive rate. The total 583 cases is examined, 418 cases are classified accurately as true Positive rate and remaining as the false negative rate. The comparative results show that the BPNN classifier outperforms SVM classifier where BPNN gives an accuracy of 73.23%, and SVM gives classification accuracy of 63.11%. This result indicates that the classification capability of BPNN is better than SVM and may potentially fill in a critical gap in the use of current or future classification algorithms for liver cancer.