This article reviews the field of image processing in recent years is enormously developed and it has been used in several specialties like medical, stand-alone, satellite and the purpose of this field is to improve image quality and extract information. Pneumonia has become in recent years a defective disease that affects the majorities of the population is especially the elderly and can sometimes put their lives in danger, in order to save human life early pneumonia diagnostic is necessary; in this work we have based on the detection and classification of patients with pneumonia from their chest x-ray. However, there are several areas where image classification is applied with success, in our work we have used deep learning based on the most common convolutional neural networks to make an image classification of pneumonia disease and to obtained good results and gave several advantages.

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Image segmentation is a significant issue in image processing. Among the various models and approaches that have been developed, some are commonly used the Markov Random Field (MRF) model, statistical techniques (MRF). In this study a Markov random field proposed is based on an EM Modified (EMM) model. In this paper, The local optimization is based on a modified Expectation-Maximization (EM) method for parameter estimation and the ICM method for finding the solution given a fixed set of these parameters. To select the combination strategy, it is necessary to carry out a comparative study to find the best result. The effectiveness of our proposed methods has been proven by experimentation. We have applied this segmented algorithm to different types of images, exhibiting the algorithm's image segmentation strength with its best values criteria for EM statics and other methods.