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
Recommended Citation
Adamjee, U., & Ghani, S. (2019). Technical Papers Session III: Malaria cell identification from microscopic blood smear images. International Conference on Information and Communication Technologies. Retrieved from https://ir.iba.edu.pk/icict/2019/2019/19
COinS
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%.
