A Comparative Analysis in Using Deep Learning Models Which Results in Efficient Image Data Augmentation

“Deep learning” is now the speediest field in the “Machine Learning (ML)” and “Deep Neural Networks fields (DNN)”. “Convolution Neural Networks (CNN)” are probably the most popular used technique for image classification and prediction among many DNN architectures. “Deep neural networks” as well as their accompanying machine learning, despite their achievements and promises, face a number of serious challenges. CNNs have achieved attempting to cut results in a variety of multiclass classification, and they still face a number of challenges, despite their broad perspectives. They are driven primarily by the vast scale of the models, which can exceed millions of features, as well as a lack of valid training data sets, and they have issues with overfitting and generalization abilities. This research study focuses on the most commonly discussed difficulty in the area of “machine learning”, which is the absence of adequate “training data” or an “unequal class imbalance” within databases. The use of such data augmentation is one method of addressing this issue. Moreover, researchers have evaluated and analysed several data augmentation approaches throughout this research. Secondary method is used for this paper to gather in depth information about deep learning methods to get results in Image data augmentation.