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Malaria Disease Detection Using CNN Technique with SGD, RMSprop and ADAM Optimizers

  • Avinash KumarEmail author
  • Sobhangi Sarkar
  • Chittaranjan Pradhan
Chapter
Part of the Studies in Big Data book series (SBD, volume 68)

Abstract

Malaria is life-threatening disease spread when an infected female Anopheles mosquito bites a person. Malaria is one of the predominant diseases in the world. There exists many drugs which make malaria a curable disease but due to inadequate technologies and equipments, we are unable to detect and cure it. The method of diagnosing malaria involves counting of parasite and red blood cells drugs physically which is a labor-intensive and error-prone process, especially if patients have to be tested several times a day. This issue can be solved by training machines to do the work of pathologists. We can the train the machine using many deep learning algorithms. Our model uses CNN based classification to classify the blood films to infected and normal blood films. The experimental result show our model works well on microscopic image and achieves an accuracy of 96.62% and the model has a lower model complexity are requires less computation time. Thus outperforming the state of art used previously.

Keywords

Malaria CNN SGD ADAM RMSprop Deep learning 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Avinash Kumar
    • 1
    Email author
  • Sobhangi Sarkar
    • 1
  • Chittaranjan Pradhan
    • 1
  1. 1.School of Computer EngineeringKIIT DUBhubaneswarIndia

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