JUCS - Journal of Universal Computer Science 28(10): 1087-1107, doi: 10.3897/jucs.81681
Improving Malaria Detection Using L1 Regularization Neural Network
expand article infoGhazala Hcini, Imen Jdey, Hela Ltifi
‡ Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan, Tunisia, Tunisia, Tunisia
Open Access

Malaria is a huge public health concern around the world. The conventional method of diagnosing malaria is for qualified technicians to visually examine blood smears for parasite-infected red blood cells under a microscope. This procedure is ineffective. It takes time and requires the expertise of a skilled specialist. The diagnosis is dependent on the individual performing the examination’s experience and understanding. This article offers a new and robust deep learning model for automatically classifying malaria cells as infected or uninfected. This approach is based on a convolutional neural network (CNN). It improved by the regularization method on a publicly available dataset which contains 27, 558 cell images with equal instances of parasitized and uninfected cells from the National Institute of health. The performance of our proposed model is 99.70% of accuracy and 0.0476 loss value.

Malaria Parasite Detection, Deep Learning, Convolutional Neural Network, Binary classification, L1 Regularization, Overfitting, Accuracy, Loss