An Improved DCNN Classification based on a Modified U-Net Segmentation Approach for Ovarian Cancer

Abstract

In clinical diagnosis, an effective classification of ovarian carcinoma types is highly essential to avoid the number of deaths worldwide. For this reason, deep convolutional neural network (DCNN) has been designed to classify ovarian carcinoma previously. Then, insufficiency of a dataset was handled by augmenting the training samples using deep semi-supervised generative learning (DSSGL). But, these augmented images directly fed to the DCNN without segmentation causes improper classification of ovarian carcinoma in a significant regions. Also, its computation burden is high. Hence in this article, an enhanced U-Net (EUNet) is proposed as a segmentation module with the DSSGL-DCNN framework for enhancing the accuracy of classifying ovarian carcinoma. This EUNet comprises different units: the inception-residual (IR) unit, the dense-inception (DI) unit, the downsampling unit and the upsampling unit to create the feature-level segmented maps for a given CT scan. But, raising the expansion ratio in the DI unit will provide several variables which make the framework more complex and slower to train. So, the feature-level probability map is also generated which is thresholded to binary and fused with the feature-level segmented maps to create the discriminative segmented sample. In ovarian carcinoma classification, the training CT images are first augmented by the DSSGL method and given to the EUNet. The resultant segmented images from EUNet are fed to the fused structure-based DCNN for categorizing the types of ovarian carcinomas effectively. Finally, the testing outcomes reveal that the DSSGL-EUNet-DCNN attains 91.63 % of accuracy for ovarian carcinoma categorization, whereas existing MLR,