marvelefe / fyproject

Malaria Detection in Blood Samples Using Convolutional Neural Networks

Malaria Detection in Blood Samples Using Convolutional Neural Networks

The ultimate goal of this work was to develop a program for detecting malaria using microscopic images of stained blood samples.

Conclusion

The Neural Network successfully trained on 1,000 images with a training and validation accuracy of 100.00% respectively and it was tested on the images of four blood samples that it had not seen before and further classified them as infected or uninfected with a 100% accuracy.

I worked solo on this program between April 2019 - July 2019 at Benson Idahosa University and the entire publication for this study can be found here on Academia