Built a deep learning CNN Model to differentiate between healthy and malaria infected cell images. Used Keras and Tensorflow Libraries to create a Convolutional Neural Network which successfully classifies blood cell images into malaria infected and uninfected category with an accuracy of 95% and a recall of 93%. Performed data augmentation & image preprocessing to improve predictions.
Dataset Source:
https://ceb.nlm.nih.gov/repositories/malaria-datasets/
Using of deep neural ensembles, recently report an improvement towards malaria parasite detection in thin-blood smear images and is published in the Peer Journal as cited herewith:
- Rajaraman S, Jaeger S, Antani SK. (2019) Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images. PeerJ 7:e6977: https://doi.org/10.7717/peerj.6977
+ Vedant Shrivastava | vedantshrivastava466@gmail.com