Improving Malaria Parasite Detection from Red Blood Cell using Deep Convolutional Neural Networks

by   Aimon Rahman, et al.

Malaria is a female anopheles mosquito-bite inflicted life-threatening disease which is considered endemic in many parts of the world. This article focuses on improving malaria detection from patches segmented from microscopic images of red blood cell smears by introducing a deep convolutional neural network. Compared to the traditional methods that use tedious hand engineering feature extraction, the proposed method uses deep learning in an end-to-end arrangement that performs both feature extraction and classification directly from the raw segmented patches of the red blood smears. The dataset used in this study was taken from National Institute of Health named NIH Malaria Dataset. The evaluation metric accuracy and loss along with 5-fold cross validation was used to compare and select the best performing architecture. To maximize the performance, existing standard pre-processing techniques from the literature has also been experimented. In addition, several other complex architectures have been implemented and tested to pick the best performing model. A holdout test has also been conducted to verify how well the proposed model generalizes on unseen data. Our best model achieves an accuracy of almost 97.77


page 6

page 10

page 11

page 13

page 21

page 22


Malaria detection in Segmented Blood Cell using Convolutional Neural Networks and Canny Edge Detection

We apply convolutional neural networks to identify between malaria infec...

COVID-19 Detection through Deep Feature Extraction

The SARS-CoV2 virus has caused a lot of tribulation to the human populat...

CIDMP: Completely Interpretable Detection of Malaria Parasite in Red Blood Cells using Lower-dimensional Feature Space

Predicting if red blood cells (RBC) are infected with the malaria parasi...

Pathological Analysis of Blood Cells Using Deep Learning Techniques

Pathology deals with the practice of discovering the reasons for disease...

Segmenting overlapped objects in images. A study to support the diagnosis of sickle cell disease

Overlapped objects are found on multiple kinds of images, they are a sou...

Please sign up or login with your details

Forgot password? Click here to reset