Abstract
(1) Background: People may be infected with an insect-borne disease (malaria) through the blood input of malaria-infected people or the bite of Anopheles mosquitoes. Doctors need a lot of time and energy to diagnose malaria, and sometimes the results are not ideal. Many researchers use CNN to classify malaria images. However, we believe that the classification performance of malaria parasites can be improved. (2) Methods: In this paper, we propose a novel method (ROENet) to automatically classify malaria parasite on the blood smear. The backbone of ROENet is the pre-trained ResNet-18. We use randomized neural networks (RNNs) as the classifier in our proposed model. Three RNNs are used in ROENet, which are random vector functional link (RVFL), Schmidt neural network (SNN), and extreme learning machine (ELM). To improve the performance of ROENet, the results of ROENet are the ensemble outputs from three RNNs. (3) Results: We evaluate the proposed ROENet by five-fold cross-validation. The specificity, F1 score, sensitivity, and accuracy are 96.68 ± 3.81%, 95.69 ± 2.65%, 94.79 ± 3.71%, and 95.73 ± 2.63%, respectively. (4) Conclusions: The proposed ROENet is compared with other state-of-the-art methods and provides the best results of these methods.
Funder
Data Science Enhancement Fund
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference30 articles.
1. Expert‐level automated malaria diagnosis on routine blood films with deep neural networks
2. Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears
3. Dcgan for handling imbalanced malaria dataset based on over-sampling technique and using cnn;Shoohi;Med. Leg. Update,2020
4. Computer-automated malaria diagnosis and quantitation using convolutional neural networks;Mehanian;Proceedings of the IEEE International Conference on Computer Vision Workshops,2017
5. Detection of Malaria Parasites in Thin Blood Smears Using CNN-Based Approach;Mukherjee,2021
Cited by
17 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献