Identification of Endometrial Cancer in Myometrium using RNN and SVM Algorithms for Accuracy and Sensitivity improvement

Abstract

The objective of this work is to compare the Recurrent Neural Network (RNN) algorithm and Support Vector Machine (SVM) algorithm in the identification of endometrial cancer based on its accuracy and sensitivity measurements. Materials and Methods: The endometrial cancer dataset, obtained from the National Institute of Endometrial Cancer Diseases (NIECE), contains 768 patient health records that were used to train (80 %) and test (20 %) the predictive model in MATLAB and the statistical analysis is done using SPSS software. For this research work 768 images were used with the pixel size of 3048×2048 and these images are taken from the pap smear slide dataset. The RNN algorithm is used and compared with the SVM algorithm. The sample size is estimated for two groups (RNN & SVM) with G-power of 80 % and 0.05 Type I/II Error rate (Alpha). Results: The predictive model using RNN algorithm shows a higher accuracy of 93.90 ± 0.3160 and sensitivity of 91.0400 ± 1.07207 followed by the significance value of 0.002 than SVM algorithm with accuracy of 88.10 ± 0.9940 and sensitivity of 86.1700 ± 1.36793 with the significance value of 0.000 using 2-tailed test in SPSS. Conclusion: Based on the outcome of the proposed work RNN classifier shows significantly better performance than the SVM classifier in the innovative detection of endometrial cancer.