Identifying Malaria Infection in Red Blood Cells using Optimized Step-Increase Convolutional Neural Network Model
Vikas Kashtriya1, Amit Doegar2, Varun Gupta3, Poonam Kashtriya4

1Vikas Kashtriya, Department of CSE, National Institute of Technical Teachers Training and Research, Chandigarh, India.

2Amit Doegar, Department of CSE, National Institute of Technical Teachers Training and Research, Chandigarh, India.

3Varun Gupta, Department of CSE, Chandigarh College of Engineering and Technology, Chandigarh, India.

4Poonam Kashtriya, Department of CSE, National Institute of Technology, Hamirpur (Himachal Pradesh), India.

Manuscript received on 05 August 2019 | Revised Manuscript received on 12 August 2019 | Manuscript Published on 26 August 2019 | PP: 813-818 | Volume-8 Issue-9S August 2019 | Retrieval Number: I11310789S19/19©BEIESP | DOI: 10.35940/ijitee.I1131.0789S19

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Abstract: A vast number of image processing and neural network approaches are currently being utilized in the analysis of various medical conditions. Malaria is a disease which can be diagnosed by examining blood smears. But when it is examined manually by the microscopist, the accuracy of diagnosis can be error-prone because it depends upon the quality of the smear and the expertise of microscopist in examining the smears. Among the various machine learning techniques, convolutional neural networks (CNN) promise relatively higher accuracy. We propose an Optimized Step-Increase CNN (OSICNN) model to classify red blood cell images taken from thin blood smear samples into infected and non-infected with the malaria parasite. The proposed OSICNN model consists of four convolutional layers and is showing comparable results when compared with other state of the art models. The accuracy of identifying parasite in RBC has been found to be 98.3% with the proposed model.

Keywords: CNN, Deep Learning, Malaria, Machine Learning, Medical Diagnosis, Neural Networks, Image Classification.
Scope of the Article: Deep Learning