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The objective of the study is to evaluate the efficiency of a multi layer neural network models built by combining Recurrent Neural Network(RNN) and Convolutional Neural Network(CNN) for solving the problem of classifying of different kind of White Blood Cells. This can have applications in the pharmaceutical and healthcare industry for automating the analysis of blood tests and other processes requiring identifying the nature of blood cells in a given image sample. It can also be used in diagnosis of various blood related diseases in patients.

Low-resolution medical images can hamper medical diagnosis seriously, especially in the analysis of retina images and specifically for the detection of macula fovea. Therefore, improving the quality of medical images and speeding up their reconstruction is particularly important for expert diagnosis. To deal with this engineering problem, our paper presents a fast medical image super-resolution (FMISR) method whereby the three hidden layers to complete feature extraction is as same as the super resolution convolution neural network. It is important that a well-designed deep learning network processes images in the low resolution instead of the high-resolution space and enables the super-resolution reconstruction to be more efficient. Sub-pixel convolution layer addition and mini-network substitution in the hidden layers are critical for improving the image reconstruction speed. While the hidden layers are proposed for ensuring reconstruction quality, our FMISR framework performs significantly faster and produces a higher resolution images. As such, the technique underlying this framework presents a high potential in retinal macular examination as it provides a good platform for the segmentation of retinal images. INDEX TERMS Super resolution, medical imaging, deep learning, medical diagnosis.