Impact of the Preprocessing Steps in Deep Learning-Based Image Classifications

Deep learning softwares are designed using artificial neural networks for various applications by training and testing them with an appropriate dataset. The raw image samples available in the dataset may contain noisy and unclear information due to radiation, heat and poor lighting conditions. Therefore, the researchers are trying to filter and enhance such noisy images through preprocessing steps for providing a valid feature information to the neural network layers included in the deep learning software. However, there are certain claims that roam around the researchers such as an image may lose some useful information when it is not preprocessed with an appropriate filter or enhancement technique. Hence, the work reviews the efficacy of the methodologies that are designed with and without a preprocessing step. Also, the work summarizes the common reasons and statements highlighted by the researchers for using and avoiding the preprocessing steps on designing a deep learning approach. The study is conducted to provide a clarity toward the requirement and non-requirement of preprocessing step in a deep learning software.