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.

Facial expression analysis (FEA) or Human Emotion Analysis (HEA) is an essential tool for human computer interaction. The nonverbal messages of humans are expressed by facial expression. In this study, an HEA system to classify seven classes of human emotions like happy, sad, angry, disgust, fear, surprise and neutral is presented. It uses Gabor filter for feature extraction and Multiple Instance Learning (MIL) for classification. Gabor filter analyzes the facial images in a localized region to extract specific frequency content in specific directions. Then, MIL classifier is used for the classification of emotions into any one of the seven emotions. The evaluation of HEA system is carried on JApanese Female Facial Expression (JAFFE) database. The overall recognition rate of the HEA system using Gabor and MIL technique is 95%.