Preprocessing Medical Images for Classification using Deep Learning Techniques

Recently, the demand for computer vision techniques is continuously rising because of the development of techniques in decision making pertaining to health sector. Image processing is a subset of computer vision which makes use of algorithms to perform vision emulation to recognize objects. In this study a novel convolutional neural network is configured based on deep learning to classifying Chest x-ray images into five major classes. It addresses an issue of insufficiency in medical images for employing deep learning for image classification. A new augmentation technique superimposing of images helps to generate more new samples from the available images using label-preserving transformations. Data augmentation technique can generate new sample data from the original data using various transforming strategies. Therefore the data augmentation technique helps in accumulating enough data for processing to obtain better performance. The main objective of superimposing of two images is to minimize redundancy and uncertainty in the output image. Therefore the superimposing carried out with original image and a set of various augmented image to obtain better accuracy. Later results of various superimposing techniques are compared and evaluated to demonstrate the better techniques. It is concluded that the proposed techniques can obtain better performance in medical image classification problem.