Image enhancement and feature extraction of cytomegalovirus image using hierarchical ranking convolutional neural network (HR-CNN)

Abstract

Human Cytomegalovirus is becoming a common issue around the globe, mainly it deals with the infection of the fetus in the womb. Digital image processing plays a vital role in various fields especially in the field of medicine to have a better quality of image of viruses in various forms. To have better clarity of images even in microscopic images there might be some flaws in detection of viruses because of the intensities which occur due to atmospheric lights, to overcome the flaws in microscopic images there comes a technique image enhancement to overcome noise in images especially distortion free images to be produced based on some image quality assessment and to reduce noise in an image without any loss of information. In this paper the proposed methodology called Hierarchical Ranking Convolution Neural Network is introduced based on Upward/Downward hierarchy and Forward/Backward Hierarchy to extract features and to provide intensified image of the virus. Image quality assessment is done with the parameters and evaluated using Mean Square Error, Peak signal to Noise Ratio, Root Mean Square Error, Structure Similarity Index, Mean Structure Similarity Index to prove the accuracy.