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It is a combination of supervised and unsupervised learning. It uses a combination of small portion of labeled data and massive collection of unlabeled to improve the prediction. The algorithm has ability to learn how to react on a particular situation based on the environment. The main aim of this method is to improve classification performance. This method is highly applicable in the healthcare sector when labeled data is not sufficiently available. It is applicable for classification of protein sequence typically due to the large size of DNA strands. Consistency enforcing strategy is mostly followed by this method [12]. It has been widely used for classification of medical images to reduce effort over labeling data [13–15]. Apart from this, for breast cancer analysis [16] and liver segmentation [17], a co-training mechanism has been applied.