Ghazala Hcini‡, Imen Jdey‡, Hela Ltifi‡Corresponding author: Ghazala Hcini ( hcinighazala@fstsbz.u-kairouan.tn ) © Ghazala Hcini, Imen Jdey, Hela Ltifi. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-ND 4.0). This license allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use. Citation:
Hcini G, Jdey I, Ltifi H (2022) Improving Malaria Detection Using L1 Regularization Neural Network. JUCS - Journal of Universal Computer Science 28(10): 1087-1107. https://doi.org/10.3897/jucs.81681 |  |
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.