Title

Technical Papers Session III: Malaria cell identification from microscopic blood smear images

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%.

Location

Room C9 (Aman Tower, 3rd floor)

Session Theme

Technical Papers Session III - Computer Vision

Session Type

Parallel Technical Session

Session Chair

Dr. Asim Ur Rehman Khan

Start Date

16-11-2019 3:50 PM

End Date

16-11-2019 4:10 PM

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Nov 16th, 3:50 PM Nov 16th, 4:10 PM

Technical Papers Session III: Malaria cell identification from microscopic blood smear images

Room C9 (Aman Tower, 3rd floor)

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%.