Malaria Detection in Blood Cell Images Using Image Processing Matlab Project with Source Code || IEEE Based Project

ABSTRACT

              Malaria is an extremely infectious disease cause due to blood parasite of genus plasmodium. Malaria is a terrible disease in the hematological region causing millions of mortality; hence the fast diagnosing is the extreme requirement of era. Conventional microscopy, which is presently “the gold Standard” for malaria detection has occasionally proved ineffective as it takes lots of time and outcomes are complicated to reproduce. Since it poses a global health problem, automation of the evaluation method is of high significance. An image processing system is able to enhance outcomes of detection of malaria parasite cell. A variety of image processing techniques are used in the proposed method. The method proceeds in steps like image transformation, classification and feature extraction. This method assists to reduce time as well as afford the accuracy to detect malaria to certain extent. There are lots of methods to detect malaria, among them manual microscopy is considered to be "the gold standard". However because of the various steps essential in manual estimation, this diagnostic technique takes too much time. Malaria infections are detected manually by pathologists who observe the microscopic images of strained blood records on glass slides and calculate the contaminated blood cells. If sample size of patient is great, there is always a possibility to detect imprecisely. There is a chance to occur human error, so computer based classification using digital image processing methods gives better outcome than the manual diagnoses of Malaria. Intend of this work is to build up a detection method to correctly detected malaria parasites present in images. In the pre-processing stages digital image processing systems are used to obtain high-quality medical images.In this project, Image Processing is used to detect the existence of Malaria Parasite. In the proposed system, various steps are used such as image transformation, feature extraction and image classification.

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Mr. Roshan P. Helonde

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