ANALYSIS OF DIFFERENT MEDICAL IMAGE DATASETS USING PIXEL INTENSITY BASED ALGORITHMS AND DEEP LEARNING

Abstract

Medical imaging is an important area of research that relies on correct analysis of large datasets. Deep learning is a class of the machine learning family based on artificial neural networks (ANNs) and the field of medical imaging analysis has in application the architectures of deep learning such as neural networks. This thesis is focused on two most important topics in various fields in computer science, where it is discussed in detail about the medical image classification algorithms by using deep learning techniques and cell detection algorithms on different cell environments. The state of art for imaging and especially medical imaging is deep learning because it is a good point to be referred for image analysis, image classifications, image segmentation, image counting, image detection etc and it solves many problems in the analysis of imaging. The general description about Image processing is the method to do many different transformations on an image in order to obtain an enhanced image or to extract some useful information from it as in our case the cell detection.