Malaria cell identification from microscopic blood smear images

Department

Department of Computer Science

Was this content written or created while at IBA?

Yes

Document Type

Conference Paper

Publication Date

11-1-2019

Author Affiliation

  • Uzair Adamjee is student of Computer Science at the Institute of Business Administration, Karachi
  • Sayeed Ghani is Associate Professor and Associate Dean FCS at Institute of Business Administration, Karachi

Conference Name

2019 8th International Conference on Information and Communication Technologies (ICICT)

Conference Location

Karachi, Pakistan

Conference Dates

16-17 November 2019

ISBN/ISSN

85081543542 (Scopus)

First Page

82

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Abstract / Description

This paper is about classifying blood smear images into malaria cell and uninfected cell. In this research, we have used two datasets which contains microscopic blood smear images and through deep learning techniques such as CNN, LeNet, ResNet we have created a model that can classify these images. We have applied these techniques individually on both datasets and on the combined data as well and have shown that when we gave different type of blood smear images to the deep learning model even in that scenario, model is able to identify patterns and learn features with an accuracy up to 94%.

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