Omparative Analysis of Pre-Trained Classifier in Augumented Approach for Ovarian Image

Abstract

Ovarian cancer is identified as one of the leading cause for increased mortality rate among women. The early diagnosis of ovarian cancer decreases the mortality rate, which demands for efficient classification technique. Conventional cell classification technique extracts multiple features for recognition of ovarian cancer from complex cell texture with identification of difference between cells. To resolve complexity associated with ovarian cancer cell texture analysis deep neural network exhibits improved performance. In deep learning technique, features are extracted automatically for identification of cell types and texture. However, Annotation based approach exhibits improved classification performance still performance need to be improved for automated cancer diagnosis. To achieve higher accuracy rather than annotation this paper proposed an augmentation of MRI ovarian image. The augmented images are pre-processed with median filtering for contrast enhancement. In next stage, ROI based image segmentation is performed followed by feature extraction. To improve classification performance of augmented images CNN model Inception V3 and Xception model is comparatively examined. The performance of Inception V3 and Xception model is evaluated with Logistics Regression and Random Forest classifiers. The comparative analysis of simulation results expressed that Xception Logistic Regression model provides higher accuracy than the Inception V3 Logistics regression, Inception V3 Random Forest and Xception Random Forest.