Comparison of Detection Method on Malaria Cell Images

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Description

Comparison of Detection Method on Malaria Cell Images

Abstract

Malaria is a deadly, infectious mosquito-borne disease caused by Plasmodium parasites. These parasites are transmitted by the bites of infected female Anopheles mosquitoes. While we won’t get into details about the disease, there are five main types of malaria. This project detects and classifies malaria using deep learning. With the regular manual diagnosis of blood smears, it is an intensive manual process requiring proper expertise in classifying and counting the parasitized and uninfected cells. Typically this may not scale well and might cause problems if we do not have the right expertise in specific regions around the world. Some advancements have been made in leveraging state-of-the-art (SOTA) image processing and analysis techniques to extract hand-engineered features and build machine learning-based classification models. However, these models are not scalable with more data being available for training and given the fact that hand-engineered features take a lot of time. Deep Learning models, or to be more specific, Convolution Neural Networks (CNNs) have proven to be really effective in a wide variety of computer vision tasks. Comparison of Detection Method on Malaria Cell Images


Existing Systems

  • Principal Component Analysis
  • SVM classifier 
  • Backpropagation

Drawbacks of Exisitng System

  • High Computational load and poor discriminatory power.
  • SVM is slow training for large feature sets.
  • Less accuracy in classification

Proposed Method

  • DTCWT and GLCM Features
  • NN Classifier
  • K-means Clustering

Advantages

  • The segmentation algorithm Proves to be simple and effective
  • Greyscale Co-occurrence matrix performed well in NN
  • Better texture and edge representation 
  • Segmentation provides better clustering efficiency

Comparison of Detection Method on Malaria Cell Images

Block Diagram 

Comparison of Detection Method on Malaria Cell Images 1


Hardware Requirements

  • system
  • 4 GB of RAM
  • 500 GB of Hard disk

Software Requirement

  • MATLAB 2014a

Comparison of Detection Method on Malaria Cell Images

REFERENCES

1.”Global Health Observatory (GHO)-Malaria”, 2011, [online] Available: http://www.who.int/gho/malaria/enl.

2. A. Mehrjou, “Automatic Malaria Diagnosis System”, pp. 205-211, 2013.

3.F. E. McKenzie, “Dependence Of Malaria Detection And Species Diagnosis By Microscopy On Parasite Density”, Am. Soc. Trop. Med. Hyg., 2008.

4.S. F. Total and U. K. Ngah, “Computer-Aided Medical Diagnosis for the Identification of Malaria Parasites”, IEEE – ICSCN India, no. 2, pp. 521-522.

5.G. Díaz, F. A. Gonzalez and E. Romero, “A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images”, J. Biomed. Inform., vol. 42, no. 2, pp. 296-307, 2009.

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