**2.3 Creation of a classification system**

Using convolution neural networks (CNN) can be less efficient in creating a classifier system mainly due to its requirement of a large dataset to learn from. Using CNN is not a very practical approach as it may not be feasible to collect a dataset containing large numbers of images. Thus an alternative method is proposed where features are extracted from the images using unsupervised deep learning and then a supervised machine learning classifier is used to learn from those features for classification. The advantage of this method is the elimination of overfitting of the class with majority data and the system can work fairly well with less number of images. Using a support vector machine (SVM) the classifier is built and pretrained models such as VGG-16, ResNet50, DenseNet −121, DenseNet −169, DenseNet-201, InceptionV3, InceptionResNet50 and Xception.
