Automatic Classification of Ovarian Cancer Types from CT Images Using Deep Semi-Supervised Generative Learning and Convolutional Neural Network
Pillai Honey Nagarajan, N. Tajunisha · Revue d intelligence artificielle · 2021
The classification of ovarian cancer types is a very challenging process for physicians' eyes.To solve this problem, this article proposes a new deep learner, which classifies ovarian cancer types from Computerized Tomography (CT) images.Firstly, a Deep Convolutional Neural Network (DCNN) model depending on AlexNet is proposed to categorize ovarian cancer from CT images.But its efficiency is not satisfactorily high.So, DCNN is built based on the fusion of AlexNet, VGG, and GoogLeNet.The fusion is carried out at the SoftMax layer by fusing the SoftMax values of each network structure using a weighted sum to obtain the overall classification outcome.But overfitting problems can occur due to an inadequate number of training images.Thus, a Deep Semi-Supervised Generative Learning with DCNN model (DSSGL-DCNN) is proposed by using a Generative Adversarial Network (GAN) which augments the training samples to solve the overfitting problem.Once the augmented dataset is obtained, the fused DCNN model is learned to classify ovarian cancer types.Further, the classified outcomes can be used as a useful guideline for physicians in medical diagnosis.Finally, the experimental results show that the DSSGL-DCNN achieves higher efficiency compared to the other DCNN architectures.